Statistical Procedures for Agricultural Research

Description

This package contains functionality for the Statistical Analysis of experimental designs applied specially for field experiments in agriculture and plant breeding.

Details

Planning of field experiments: lattice, factorial, RCBD, CRD, Latin Square, Youden, Graeco, BIB, Alpha design, Cyclic, augmented block, split and strip plot Designs. Comparison of multi-location trials: AMMI, Index AMMI Stability (biplot, triplot), comparison between treatments: LSD, Bonferroni and other p-adjust, HSD, Waller, Student Newman Keuls SNK, Duncan, REGW, Scheffe; Non parametric tests: Kruskal, Friedman, Durbin, Van Der Waerden, Median. Analysis of genetic experiments: North Carolina designs, LinexTester, Balanced Incomplete Block, Strip plot, Split-Plot, Partially Balanced Incomplete Block, analysis Mother and baby trials (see data RioChillon). Resampling and simulation: resampling.model, simulation.model, montecarlo, lateblight Simulator for potato. Ecology: Biodiversity Index, Path Analysis. Soil Uniformity: Smith's Index. Cluster Analysis: Consensus Cluster. Descriptive statistics utilities: *.freq

Author(s)

Felipe de Mendiburu Statistical Engineer Master in Systems Engineering Professor of Applied Statistics

Maintainer: Felipe de Mendiburu <fmendiburu@lamolina.edu.pe>

References

De Mendiburu, Felipe (2009). Una herramienta de analisis estadistico para la investigacion agricola. Tesis. Universidad Nacional de Ingenieria (UNI-PERU).

Universidad Nacional Agraria La Molina, Lima-PERU. Facultad de Economia y Planificacion Departamento Academico de Estadistica e Informatica

AMMI Analysis

Description

Additive Main Effects and Multiplicative Interaction Models (AMMI) are widely used to analyze main effects and genotype by environment (GEN, ENV) interactions in multilocation variety trials. Furthermore, this function generates data to biplot, triplot graphs and analysis.

Usage

AMMI(ENV, GEN, REP, Y, MSE = 0,console=FALSE,PC=FALSE)

Arguments

Details

additional graphics see help(plot.AMMI).

Value

Author(s)

F. de Mendiburu

References

Crossa, J. 1990. Statistical analysis of multilocation trials. Advances in Agronomy 44:55-85

See Also

lineXtester,plot.AMMI

Examples

# Full replications
library(agricolae)
# Example 1
data(plrv)
model<- with(plrv,AMMI(Locality, Genotype, Rep, Yield, console=FALSE))
model$ANOVA
# see help(plot.AMMI)
# biplot
plot(model)
# biplot PC1 vs Yield 
plot(model, first=0,second=1, number=TRUE)
# Example 2
data(CIC)
data1<-CIC$comas[,c(1,6,7,17,18)]
data2<-CIC$oxapampa[,c(1,6,7,19,20)]
cic <- rbind(data1,data2)
model<-with(cic,AMMI(Locality, Genotype, Rep, relative))
model$ANOVA
plot(model,0,1,angle=20,ecol="brown")
# Example 3
# Only means. Mean square error is well-known.
data(sinRepAmmi)
REP <- 3
MSerror <- 93.24224
#startgraph
model<-with(sinRepAmmi,AMMI(ENV, GEN, REP, YLD, MSerror,PC=TRUE))
# print anova
print(model$ANOVA,na.print = "")
# Biplot with the one restored observed.
plot(model,0,1)
# with principal components model$PC is class "princomp" 
pc<- model$PC
pc$loadings
summary(pc)
biplot(pc)
# Principal components by means of the covariance similar AMMI
# It is to compare results with AMMI
cova<-cov(model$genXenv)
values<-eigen(cova)
total<-sum(values$values)
round(values$values*100/total,2)
# AMMI: 64.81 18.58 13.50  3.11  0.00

AMMI contour

Description

Draws a polygon or a circumference around the center of the Biplot with a proportional radio at the longest distance of the genotype.

Usage

AMMI.contour(model, distance, shape, ...)

Arguments

Details

First, it is necessary to execute the AMMI function. It is only valid for the BIPLOT function but not for the TRIPLOT one.

Value

Genotypes within and outside the area.

Note

Complement graphics AMMI

Author(s)

Felipe de Mendiburu

See Also

AMMI

Examples

library(agricolae)
# see AMMI.
data(sinRepAmmi)
Environment <- sinRepAmmi$ENV
Genotype <- sinRepAmmi$GEN
Yield <- sinRepAmmi$YLD
REP <- 3
MSerror <- 93.24224
model<-AMMI(Environment, Genotype, REP, Yield, MSerror)
plot(model)
AMMI.contour(model,distance=0.7,shape=8,col="red",lwd=2,lty=5)

Finding the Variance Analysis of the Balanced Incomplete Block Design

Description

Analysis of variance BIB and comparison mean adjusted.

Usage

BIB.test(block, trt, y, test = c("lsd","tukey","duncan","waller","snk"), 
alpha = 0.05, group = TRUE,console=FALSE)

Arguments

Details

Test of comparison treatment. lsd: Least significant difference. tukey: Honestly significant differente. duncan: Duncan's new multiple range test waller: Waller-Duncan test. snk: Student-Newman-Keuls (SNK)

Value

Author(s)

F. de Mendiburu

References

Design of Experiments. Robert O. Kuehl. 2nd ed., Duxbury, 2000 Linear Estimation and Design of Experiments. D.D. Joshi. WILEY EASTERN LIMITED 1987, New Delhi, India. Introduction to experimental statistics. Ching Chun Li McGraw - Hill Book Company, Inc. New York. 1964

See Also

DAU.test, duncan.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
# Example Design of Experiments. Robert O. Kuehl. 2da. Edicion. 2001
run<-gl(10,3)
psi<-c(250,325,475,250,475,550,325,400,550,400,475,550,325,475,550,
250,400,475,250,325,400,250,400,550,250,325,550,325,400,475)
monovinyl<-c(16,18,32,19,46,45,26,39,61,21,35,55,19,47,48,20,33,31,13,13,34,21,
 30,52,24,10,50,24,31,37)
out<-BIB.test(run,psi,monovinyl,test="waller",group=FALSE)
print(out)
bar.err(out$means,variation="range",ylim=c(0,60),bar=FALSE,col=0)
out<-BIB.test(run,psi,monovinyl,test="waller",group=TRUE)
out<-BIB.test(run,psi,monovinyl,test="tukey",group=TRUE,console=TRUE)
out<-BIB.test(run,psi,monovinyl,test="tukey",group=FALSE,console=TRUE)
rm(run,psi,monovinyl,out)
# Example linear estimation and design of experiments. D.D. Joshi. 1987
# Professor of Statistics, Institute of Social Sciences Agra, India
# 6 varieties of wheat crop in a BIB whit 10 blocks of 3 plots each.
y <-c(69,77,72,63,70,54,65,65,57,59,50,45,68,75,59,38,60,60,62,
 55,54,65,62,65,61,39,54,67,63,56)
varieties<-gl(6,5)
block <- c(1,2,3,4,5,1,2,6,7,8,1,3,6,9,10,2,4,7,9,10,3,5,7,8,9,4,5,6,8,10)
BIB.test(block, varieties, y)
# Example Introduction to experimental statistics. Ching Chun Li. 1964
# pag. 395 table. 27.2
# 7 trt, k=3 and b=7.
y <-c(10,15,11,4,12,15,5,14,10,14,19,19,8,10,17,6,11,12,5,14,21)
block<-gl(7,3)
trt <- c(1,2,4,2,3,5,3,4,6,4,5,7,1,5,6,2,6,7,1,3,7)
out<-BIB.test(block, trt, y, test="duncan")
bar.group(out$groups,col="blue",density=4,ylim=c(0,max(y)))
rm(y,block,trt,out)

Data for late blight of potatoes

Description

A study of Phytophthora infestans in the potato plant in the localities of Comas and Oxapampa in Peru, 2005.

Usage

data(CIC)

Format

The format is: List of 2 (comas, oxapampa)

Locality

a factor with levels Comas Oxapampa

Genotype

a factor

Rep

a numeric vector, replications

E9

a numeric vector, infestans percentaje to 9 days

AUDPC

a numeric vector: the area under the disease-progress curve

Relative

a numeric vector, relative area

Details

comas: temperature=59.9 Fahrenheit, relative humidity=83.3 oxapampa: temperature=64.8 Fahrenheit, relative humidity=86.2 AUDPC and relative see function audpc(). help(audpc) Exx: Evaluation in percentaje, xx is days. ORD1, ORD2, SBLK and row are references location of the plot in the field.

Source

Experimental field, 2004-2005. Data Kindly provided by Matilde Orrillo.

References

International Potato Center. CIP - Lima Peru.

Examples

library(agricolae)
data(CIC)
CIC$comas
CIC$oxapampa

Data amendment Carhuaz 2006

Description

Incidents and performance of healthy tubers and rotten potato field infested with naturally Ralstonia solanacearum Race 3/Bv 2A, after application of inorganic amendments and a rotation crop in Carhuaz Peru, 2006.

Usage

data(Chz2006)

Format

The format is: List of 2

amendment

a factor

crop

a factor

block

a numeric vector, replications

plant

a numeric vector, number plant

wilt_percent

a numeric vector, wilt percentage at 60 days

health

a numeric vector, kg/8m2

rot

a numeric vector, kg/8m2

Details

Application of inorganic amendment and crop rotation to control bacterial wilt of the potato (MBP).

Source

Experimental field, 2006. Data Kindly provided by Pedro Aley.

References

International Potato Center. CIP - Lima Peru.

Examples

library(agricolae)
data(Chz2006)
str(Chz2006)
wilt<-Chz2006$wilt
yield<-Chz2006$yield
means <- tapply.stat(wilt[,5],wilt[,1:3],function(x) mean(x,na.rm=TRUE))
names(means)[4]<-"wilt_percent"
model <- aov(wilt_percent ~ block + crop, means)
anova(model)
cv.model(model)
yield<-yield[order(paste(yield[,1],yield[,2],yield[,3])),]
correlation(means[,4],yield[,4],method="spearman")

Data AUDPC Comas - Oxapampa

Description

Fifty-three potato varieties developed by the breeding program of the International Potato Center and released in different countries around the world were evaluated for their resistance to late blight in two locations in Peru.

Usage

data(ComasOxapampa)

Format

A data frame with 168 observations on the following 4 variables.

cultivar

a factor with 56 levels

replication

a factor with 3 levels

comas

a numeric vector

oxapampa

a numeric vector

Details

The experimental design was a randomized complete block design with 3 replications of 15 apical stem cuttings in Oxapampa and 10 tubers in Mariscal Castilla. Plots were 11.9 x 18.5 m in size with 30 cm in-row and 0.9 m between-row spacings. Spreader rows around plots were used at each site. Mancozeb was applied weekly until 30 days after transplanting or planting, after which the plants were left to natural infection. Due to climatic conditions not conductive to the disease in Oxapampa, inoculum was enhanced with local isolate (POX 067, with virulence R1, 2, 3, 4, 5, 6, 7, 10, 11) at a concentration of 5000-sporangia/ ml at 49 days after planting. Percentage of foliar infection was estimated visually every 3 days for 8 times in Oxapampa and every 7 days for 12 times in Comas, then values were converted to the relative area under the diseases progress curve (rAUPDC). rAUDPC rankings were analyzed for phenotypic stability with nonparametric measures.

Source

Experimental field, 2002. Data Kindly provided by Wilmer Perez.

References

International Potato Center. CIP - Lima Peru.

Examples

library(agricolae)
data(ComasOxapampa)
# Oxapampa (10 35 31 S latitude, 75 23 0 E longitude, 1813 m.a.s.l )
# Comas, Mariscal Castilla (11  42  54  S latitude, 75 04 45 E longitude, 2800 m.a.s.l,)
# cultivars LBr-40 (resistant), Cruza 148 (moderately resistant) and Pimpernell (susceptible)
str(ComasOxapampa)
means <- tapply.stat(ComasOxapampa[,3:4],ComasOxapampa$cultivar,mean)
correlation(means$comas,means$oxapampa, method="kendall")

Finding the Variance Analysis of the Augmented block Design

Description

Analysis of variance Augmented block and comparison mean adjusted.

Usage

DAU.test(block, trt, y, method = c("lsd","tukey"),alpha=0.05,group=TRUE,console=FALSE)

Arguments

Details

Method of comparison treatment. lsd: Least significant difference. tukey: Honestly significant differente. The controls can have different repetitions, at least two, do not use missing data.

Value

Author(s)

F. de Mendiburu

References

Federer, W. T. (1956). Augmented (or hoonuiaku) designs. Hawaiian Planters, Record LV(2):191-208.

See Also

BIB.test, duncan.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
block<-c(rep("I",7),rep("II",6),rep("III",7))
trt<-c("A","B","C","D","g","k","l","A","B","C","D","e","i","A","B","C","D","f","h","j")
yield<-c(83,77,78,78,70,75,74,79,81,81,91,79,78,92,79,87,81,89,96,82)
out<- DAU.test(block,trt,yield,method="lsd", group=TRUE)
print(out$groups)
plot(out)

Data for the analysis of carolina genetic design

Description

Data for the analysis of carolina I, II and III genetic design

Usage

data(DC)

Details

DC is list, 3 data.frame: carolina1(72 obs, 6 var), carolina2(300 obs, 9 var) and carolina3(64 obs, 5 var).

Carolina1: Data for the analysis of Carolina I Genetic design. In this design F2 or any advanced generation maintained by random mating, produced from cross between two pure-lines, is taken as base population. From the population an individual is randomly selected and used as a male. A set of 4 randomly selected plans are used as females and are mated to the above male. Thus a set of 4 full-sib families are produced. This is denoted as a male group. Similarly, a large number of male groups are produced. No female is used for any second mating. four male groups (16 female groups) from a set.

Carolina2: Data for the analysis of Carolina II Genetic design. Both paternal and maternal half-sibs are produced in this design. From an F2 population, n1 males and n2 females are randomly selected and each male is crossed to each of the females. Thus n1 x n2 progenies are produced whitch are analysed in a suitably laid experiment.

Carolina3: Data for the analysis of Carolina III genetic design. The F2 population is produced by crossing two inbreds, say L1 and L2. The material for estimation of genetic parameters is produced by back crossing randomly selected F2 individuals (using as males) to each of the inbreds (used as females).

Source

Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979.

References

Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979.

Examples

data(DC)
names(DC)
str(DC$carolina1)
str(DC$carolina2)
str(DC$carolina3)

Data Glycoalkaloids

Description

A measurement of the Glycoalkaloids by two methods: HPLC and spectrophotometer.

Usage

data(Glycoalkaloids)

Format

A data frame with 25 observations on the following 2 variables.

HPLC

a numeric vector

spectrophotometer

a numeric vector

Source

International Potato Center. CIP - Lima Peru.

References

International Potato Center. CIP - Lima Peru.

Examples

library(agricolae)
data(Glycoalkaloids)
str(Glycoalkaloids)

Multiple comparisons: Tukey

Description

It makes multiple comparisons of treatments by means of Tukey. The level by alpha default is 0.05.

Usage

HSD.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL, 
unbalanced=FALSE,console=FALSE)

Arguments

Details

It is necessary first makes a analysis of variance.

if y = model, then to apply the instruction:
HSD.test (model, "trt", alpha = 0.05, group = TRUE, main = NULL, unbalanced=FALSE, console=FALSE)
where the model class is aov or lm.

Value

Author(s)

Felipe de Mendiburu

References

1. Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third Edition 1997
2. Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

See Also

BIB.test, DAU.test, duncan.test, durbin.test, friedman, kruskal, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
data(sweetpotato)
model<-aov(yield~virus, data=sweetpotato)
out <- HSD.test(model,"virus", group=TRUE,console=TRUE,
main="Yield of sweetpotato\nDealt with different virus")
#stargraph
# Variation range: max and min
plot(out)
#endgraph
out<-HSD.test(model,"virus", group=FALSE)
print(out$comparison)
# Old version HSD.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
with(sweetpotato,HSD.test(yield,virus,df,MSerror, group=TRUE,console=TRUE,
main="Yield of sweetpotato. Dealt with different virus"))

Data amendment Huanuco 2006

Description

Incidents and performance of healthy tubers and rotten potato field infested with naturally Ralstonia solanacearum Race 3/Bv 2A, after application of inorganic amendments and a rotation crop in Huanuco Peru, 2006.

Usage

data(Hco2006)

Format

The format is: List of 2

amendment

a factor

crop

a factor

block

a numeric vector, replications

plant

a numeric vector, number plant

wilt_percent

a numeric vector, wilt percentage at 60 days

health

a numeric vector, kg/8m2, 20 plants

rot

a numeric vector, kg/8m2, 20 plants

Details

Application of inorganic amendment and crop rotation to control bacterial wilt of the potato (MBP).

Source

Experimental field, 2006. Data Kindly provided by Pedro Aley.

References

International Potato Center. CIP - Lima Peru.

Examples

library(agricolae)
data(Hco2006)
str(Hco2006)
wilt<-Hco2006$wilt
yield<-Hco2006$yield
means <- tapply.stat(wilt[,5],wilt[,1:3],function(x) mean(x,na.rm=TRUE))
names(means)[4]<-"wilt_percent"
model <- aov(wilt_percent ~ block + crop, means)
anova(model)
cv.model(model)
yield<-yield[order(paste(yield[,1],yield[,2],yield[,3])),]
correlation(means[,4],yield[,4],method="spearman")

Multiple comparisons, "Least significant difference" and Adjust P-values

Description

Multiple comparisons of treatments by means of LSD and a grouping of treatments. The level by alpha default is 0.05. Returns p-values adjusted using one of several methods

Usage

LSD.test(y, trt, DFerror, MSerror, alpha = 0.05, p.adj=c("none","holm","hommel", 
"hochberg", "bonferroni", "BH", "BY", "fdr"), group=TRUE, main = NULL,console=FALSE)

Arguments

Details

For equal or different repetition.
For the adjustment methods, see the function p.adjusted.
p-adj ="none" is t-student.

It is necessary first makes a analysis of variance.
if model=y, then to apply the instruction:
LSD.test(model, "trt", alpha = 0.05, p.adj=c("none","holm","hommel", "hochberg", "bonferroni", "BH", "BY", "fdr"), group=TRUE, main = NULL,console=FALSE)
where the model class is aov or lm.

Value

Author(s)

Felipe de Mendiburu

References

Steel, R.; Torri,J; Dickey, D.(1997) Principles and Procedures of Statistics A Biometrical Approach. pp178.

See Also

BIB.test, DAU.test, duncan.test, durbin.test, friedman, HSD.test, kruskal, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
data(sweetpotato)
model<-aov(yield~virus, data=sweetpotato)
out <- LSD.test(model,"virus", p.adj="bonferroni")
#stargraph
# Variation range: max and min
plot(out)
#endgraph
# Old version LSD.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
out <- with(sweetpotato,LSD.test(yield,virus,df,MSerror))
#stargraph
# Variation interquartil range: Q75 and Q25
plot(out,variation="IQR")
#endgraph
out<-LSD.test(model,"virus",p.adj="hommel",console=TRUE)
plot(out,variation="SD") # variation standard deviation

Data Line by tester

Description

Data frame with yield by line x tester.

Usage

data(LxT)

Format

A data frame with 92 observations on the following 4 variables.

replication

a numeric vector

line

a numeric vector

tester

a numeric vector

yield

a numeric vector

Source

Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979

Median test. Multiple comparisons

Description

A nonparametric test for several independent samples. The median test is designed to examine whether several samples came from populations having the same median.

Usage

Median.test(y,trt,alpha=0.05,correct=TRUE,simulate.p.value = FALSE, group = TRUE, 
main = NULL,console=TRUE)

Arguments

Details

The data consist of k samples of possibly unequal sample size.
Greater: is the number of values that exceed the median of all data and
LessEqual: is the number less than or equal to the median of all data.

Value

Author(s)

Felipe de Mendiburu

References

Practical Nonparametrics Statistics. W.J. Conover, 1999

See Also

BIB.test, DAU.test, duncan.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
# example 1
data(corn)
out<-with(corn,Median.test(observation,method,console=FALSE))
z<-bar.err(out$medians,variation = "range",ylim=c(0,120),
           space=2,border=4,col=3,density=10,angle=45)
# example 2
out<-with(corn,Median.test(observation,method,console=FALSE,group=FALSE))
print(out$comparison)

Analysis of the Partially Balanced Incomplete Block Design

Description

Analysis of variance PBIB and comparison mean adjusted. Applied to resoluble designs: Lattices and alpha design.

Usage

PBIB.test(block,trt,replication,y,k, method=c("REML","ML","VC"), 
test = c("lsd","tukey"), alpha=0.05, console=FALSE, group=TRUE)

Arguments

Details

Method of comparison treatment. lsd: least significant difference.
tukey: Honestly significant difference.
Estimate: specifies the estimation method for the covariance parameters.
The REML is the default method. The REML specification performs residual (restricted) maximum likelihood, and The ML specification performs maximum likelihood, and the VC specifications apply only to variance component models.
The PBIB.test() function can be called inside a function (improvement by Nelson Nazzicari, Ph.D. Bioinformatician)

Value

Author(s)

F. de Mendiburu

References

1. Iterative Analysis of Generalizad Lattice Designs. E.R. Williams (1977) Austral J. Statistics 19(1) 39-42.

2. Experimental design. Cochran and Cox. Second edition. Wiley Classics Library Edition published 1992

See Also

BIB.test, DAU.test, duncan.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, Median.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

require(agricolae)
# alpha design 
Genotype<-c(paste("gen0",1:9,sep=""),paste("gen",10:30,sep=""))
ntr<-length(Genotype)
r<-2
k<-3
s<-10
obs<-ntr*r
b <- s*r
book<-design.alpha(Genotype,k,r,seed=5)
book$book[,3]<- gl(20,3)
dbook<-book$book
# dataset
yield<-c(5,2,7,6,4,9,7,6,7,9,6,2,1,1,3,2,4,6,7,9,8,7,6,4,3,2,2,1,1,2,
     1,1,2,4,5,6,7,8,6,5,4,3,1,1,2,5,4,2,7,6,6,5,6,4,5,7,6,5,5,4)
rm(Genotype)
# not run
# analysis
# require(nlme) # method = REML or LM in PBIB.test and require(MASS) method=VC
model <- with(dbook,PBIB.test(block, Genotype, replication, yield, k=3, method="VC"))
# model$ANOVA
# plot(model,las=2)

Ryan, Einot and Gabriel and Welsch multiple range test

Description

Multiple range tests for all pairwise comparisons, to obtain a confident inequalities multiple range tests.

Usage

REGW.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL,console=FALSE)

Arguments

Details

It is necessary first makes a analysis of variance.

if y = model, then to apply the instruction:
REGW.test (model, "trt", alpha = 0.05, group = TRUE, main = NULL, console = FALSE)
where the model class is aov or lm.

Value

Author(s)

Felipe de Mendiburu

References

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC

See Also

BIB.test, DAU.test, duncan.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, Median.test, PBIB.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
out<- REGW.test(model,"virus", 
main="Yield of sweetpotato. Dealt with different virus")
print(out)
REGW.test(model,"virus",alpha=0.05,console=TRUE,group=FALSE)

Data and analysis Mother and baby trials

Description

Mother/Baby Trials allow farmers and researchers to test best-bet technologies or new cultivars. Evaluation of advanced Clones of potato in the Valley of Rio Chillon - PERU (2004)

Usage

data(RioChillon)

Format

The format is list of 2:
1. mother: data.frame: 30 obs. of 3 variables:
- block (3 levels)
- clon (10 levels)
- yield (kg.)
2. babies: data.frame: 90 obs. of 3 variables:
- farmer (9 levels)
- clon (10 levels)
- yield (kg.)

Details

1. Replicated researcher-managed "mother trials" with typically 10 treatments evaluated in small plots.
2. Unreplicated "baby trials" with 10 treatments evaluated in large plots.
3. The "baby trials" with a subset of the treatments in the mother trial.

Source

Experimental field.

References

International Potato Center. CIP - Lima Peru.

Examples

# Analisys the Mother/Baby Trial Design
library(agricolae)
data(RioChillon)
# First analysis the Mother Trial Design.
model<-aov(yield ~ block + clon, RioChillon$mother)
anova(model)
cv.model(model)
comparison<-with(RioChillon$mother,LSD.test(yield,clon, 18, 4.922, group=TRUE))
# Second analysis the babies Trial.
comparison<-with(RioChillon$babies,friedman(farmer,clon, yield, group=TRUE))
# Third
# The researcher makes use of data from both mother and baby trials and thereby obtains
# information on suitability of new technologies or cultivars
# for different agro-ecologies and acceptability to farmers.
 

Student-Newman-Keuls (SNK)

Description

SNK is derived from Tukey, but it is less conservative (finds more differences). Tukey controls the error for all comparisons, where SNK only controls for comparisons under consideration. The level by alpha default is 0.05.

Usage

SNK.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL,console=FALSE)

Arguments

Details

It is necessary first makes a analysis of variance.

if y = model, then to apply the instruction:
SNK.test (model, "trt", alpha = 0.05, group = TRUE, main = NULL, console = FALSE)
where the model class is aov or lm.

Value

Author(s)

Felipe de Mendiburu

References

1. Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third Edition 1997
2. Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

See Also

BIB.test, DAU.test, duncan.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
out <- SNK.test(model,"virus", console=TRUE, 
main="Yield of sweetpotato. Dealt with different virus")
print(SNK.test(model,"virus", group=FALSE))
# version old SNK.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
out <- with(sweetpotato,SNK.test(yield,virus,df,MSerror, group=TRUE))
print(out$groups)

Calculating the absolute or relative value of the AUDPC

Description

Area Under Disease Progress Curve. The AUDPC measures the disease throughout a period. The AUDPC is the area that is determined by the sum of trapezes under the curve.

Usage

audpc(evaluation, dates, type = "absolute")

Arguments

Details

AUDPC. For the illustration one considers three evaluations (14, 21 and 28 days) and percentage of damage in the plant 40, 80 and 90 (interval between dates of evaluation 7 days). AUDPC = 1045. The evaluations can be at different interval.

Value

Vector with relative or absolute audpc.

Author(s)

Felipe de Mendiburu

References

Campbell, C. L., L. V. Madden. (1990): Introduction to Plant Disease Epidemiology. John Wiley & Sons, New York City.

Examples

library(agricolae)
dates<-c(14,21,28) # days
# example 1: evaluation - vector
evaluation<-c(40,80,90)
audpc(evaluation,dates)
# example 2: evaluation: dataframe nrow=1
evaluation<-data.frame(E1=40,E2=80,E3=90) # percentages
plot(dates,evaluation,type="h",ylim=c(0,100),col="red",axes=FALSE)
title(cex.main=0.8,main="Absolute or Relative AUDPC\nTotal area = 100*(28-14)=1400")
lines(dates,evaluation,col="red")
text(dates,evaluation+5,evaluation)
text(18,20,"A = (21-14)*(80+40)/2")
text(25,60,"B = (28-21)*(90+80)/2")
text(25,40,"audpc = A+B = 1015")
text(24.5,33,"relative = audpc/area = 0.725")
abline(h=0)
axis(1,dates)
axis(2,seq(0,100,5),las=2)
lines(rbind(c(14,40),c(14,100)),lty=8,col="green")
lines(rbind(c(14,100),c(28,100)),lty=8,col="green")
lines(rbind(c(28,90),c(28,100)),lty=8,col="green")
# It calculates audpc absolute
absolute<-audpc(evaluation,dates,type="absolute")
print(absolute)
rm(evaluation, dates, absolute)
# example 3: evaluation dataframe nrow>1
data(disease)
dates<-c(1,2,3) # week
evaluation<-disease[,c(4,5,6)]
# It calculates audpc relative
index <-audpc(evaluation, dates, type = "relative")
# Correlation between the yield and audpc
correlation(disease$yield, index, method="kendall")
# example 4: days infile
data(CIC)
comas <- CIC$comas
oxapampa <- CIC$oxapampa
dcomas <- names(comas)[9:16]
days<- as.numeric(substr(dcomas,2,3))
AUDPC<- audpc(comas[,9:16],days)
relative<-audpc(comas[,9:16],days,type = "relative")
h1<-graph.freq(AUDPC,border="red",density=4,col="blue")
table.freq(h1)
h2<-graph.freq(relative,border="red",density=4,col="blue",
frequency=2, ylab="relative frequency")

The Area Under the Disease Progress Stairs

Description

A better estimate of disease progress is the area under the disease progress stairs (AUDPS). The AUDPS approach improves the estimation of disease progress by giving a weight closer to optimal to the first and last observations.

Usage

audps(evaluation, dates, type = "absolute")

Arguments

Details

AUDPS. For the illustration one considers three evaluations (14, 21 and 28 days) and percentage of damage in the plant 40, 80 and 90 (interval between dates of evaluation 7 days). AUDPS = 1470. The evaluations can be at different interval. AUDPS= sum( rectangle area by interval in times evaluation ) see example.

Value

Vector with relative or absolute audps.

Author(s)

Felipe de Mendiburu

References

Ivan Simko, and Hans-Peter Piepho, (2012). The area under the disease progress stairs: Calculation, advantage, and application. Phytopathology 102:381- 389.

Examples

library(agricolae)
dates<-c(14,21,28) # days
# example 1: evaluation - vector
evaluation<-c(40,80,90)
audps(evaluation,dates)
audps(evaluation,dates,"relative")
x<-seq(10.5,31.5,7)
y<-c(40,80,90,90)
plot(x,y,"s",ylim=c(0,100),xlim=c(10,32),axes=FALSE,col="red" ,ylab="",xlab="")
title(cex.main=0.8,main="Absolute or Relative AUDPS\nTotal area=(31.5-10.5)*100=2100",
ylab="evaluation",xlab="dates" )
points(x,y,type="h")
z<-c(14,21,28)
points(z,y[-3],col="blue",lty=2,pch=19)
points(z,y[-3],col="blue",lty=2,pch=19)
axis(1,x,pos=0)
axis(2,c(0,40,80,90,100),las=2)
text(dates,evaluation+5,dates,col="blue")
text(14,20,"A = (17.5-10.5)*40",cex=0.8)
text(21,40,"B = (24.5-17.5)*80",cex=0.8)
text(28,60,"C = (31.5-24.5)*90",cex=0.8)
text(14,95,"audps = A+B+C = 1470")
text(14,90,"relative = audps/area = 0.7")
# It calculates audpc absolute
absolute<-audps(evaluation,dates,type="absolute")
print(absolute)
rm(evaluation, dates, absolute)

Plotting the standard error or standard deviance of a multiple comparison of means

Description

It plots bars of the averages of treatments and standard error or standard deviance. It uses the objects generated by a procedure of comparison like LSD, HSD, Kruskal and Waller-Duncan.

Usage

bar.err(x,variation=c("SE","SD","range","IQR"),horiz=FALSE, bar=TRUE,...)

Arguments

Details

x: data frame formed by 5 columns: name of the bars, height, level out: LSD.test, HSD, waller.test, scheffe.test, duncan.test, SNK.test, friedman, kruskal, waerden.test and Median.test.

Value

A list with numeric vectors giving the coordinates of all the bar midpoints drawn.

Author(s)

Felipe de Mendiburu

See Also

LSD.test, HSD.test, waller.test, kruskal, bar.group

Examples

library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
out <- waller.test(model,"virus", console=TRUE,
main="Yield of sweetpotato\ndealt with different virus")
oldpar<-par(mfrow=c(2,2),cex=1)
bar.err(out$means,variation="range",horiz=TRUE,xlim=c(0,45),angle=125,density=6,
 main="range")
bar.err(out$means,variation="SD",ylim=c(0,45),col=colors()[30],
 main="Standard deviation",density=8)
bar.err(out$means,variation="SE",horiz=TRUE,xlim=c(0,45),density=8,
 col="brown",main="Standard error")
bar.err(out$means,variation="range",ylim=c(0,45),bar=FALSE,col="green",
 main="range")
par(oldpar)
oldpar<-par(mfrow=c(1,2),cex=1)
bar.err(out$means,variation="range",ylim=c(0,45),bar=FALSE,col=0)
abline(h=0)
# horiz = TRUE
bar.err(out$means,variation="SE",horiz=TRUE,xlim=c(0,45),bar=FALSE,col=0)
#startgraph
par(oldpar)
#endgraph

Plotting the multiple comparison of means

Description

It plots bars of the averages of treatments to compare. It uses the objects generated by a procedure of comparison like LSD, HSD, Kruskall, Waller-Duncan, Friedman or Durbin. It can also display the 'average' value over each bar in a bar chart.

Usage

bar.group(x,horiz=FALSE, decreasing=TRUE, ...)

Arguments

Details

x: data frame formed by 5 columns: name of the bars, height and level of the bar.

Value

A list with numeric vectors giving the coordinates of all the bar midpoints drawn.

Author(s)

Felipe de Meniburu

See Also

LSD.test, HSD.test, kruskal , friedman, durbin.test, waller.test , plot.group

Examples

# Example 1
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
comparison<- LSD.test(model,"virus",alpha=0.01,group=TRUE)
print(comparison$groups)
oldpar<-par(cex=1.5)
bar.group(comparison$groups,horiz=TRUE,density=8,col="blue",border="red", xlim=c(0,50),las=1)
title(cex.main=0.8,main="Comparison between\ntreatment means",xlab="Yield",ylab="Virus")
# Example 2
library(agricolae)
x <- 1:4
y <- c(0.29, 0.44, 0.09, 0.49)
xy <- data.frame(x,y,y)
par(oldpar)
oldpar<-par(cex=1.5)
bar.group(xy,density=30,angle=90,col="brown",border=FALSE,ylim=c(0,0.6),lwd=2,las=1)
par(oldpar)

North Carolina Designs I, II and III

Description

Statistic analysis of the Carolina I, II and III genetic designs.

Usage

carolina(model,data)

Arguments

Details

model = 1,2 and 3 is I, II and III see carolina1,2 and 3.

Value

and variance and additive variance of male, female and male.female interaction.

Author(s)

Felipe de Mendiburu

References

Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979

See Also

DC

Examples

library(agricolae)
data(DC)
carolina1 <- DC$carolina1
# str(carolina1)
output<-carolina(model=1,carolina1)
output[][-1]

carolina2 <- DC$carolina2
# str(carolina2)
majes<-subset(carolina2,carolina2[,1]==1)
majes<-majes[,c(2,5,4,3,6:8)]
output<-carolina(model=2,majes[,c(1:4,6)])
output[][-1]

carolina3 <- DC$carolina3
# str(carolina3)
output<-carolina(model=3,carolina3)
output[][-1]

Data of Ralstonia population in clay soil

Description

An evaluation over a time period.

Usage

data(clay)

Format

A data frame with 69 observations on the following 3 variables.

per.clay

a numeric vector

days

a numeric vector

ralstonia

a numeric vector

Source

Experimental field.

References

International Potato Center. CIP - Lima Peru.

Examples

library(agricolae)
data(clay)
str(clay)

consensus of clusters

Description

The criterion of the consensus is to produce many trees by means of boostrap and to such calculate the relative frequency with members of the clusters.

Usage

consensus(data,distance=c("binary","euclidean","maximum","manhattan",
"canberra", "minkowski", "gower","chisq"),method=c("complete","ward","single","average",
"mcquitty","median", "centroid"),nboot=500,duplicate=TRUE,cex.text=1, 
col.text="red", ...)

Arguments

Details

distance: "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "gower", "chisq". Method: "ward", "single", "complete", "average", "mcquitty", "median", "centroid". see functions: dist(), hclust() and daisy() of cluster.

Value

Author(s)

F. de Mendiburu

References

An Introduction to the Boostrap. Bradley Efron and Robert J. Tibshirani. 1993. Chapman and Hall/CRC

See Also

hclust, hgroups, hcut

Examples

library(agricolae)
data(pamCIP)
# only code
rownames(pamCIP)<-substr(rownames(pamCIP),1,6)
output<-consensus( pamCIP,distance="binary", method="complete",nboot=5)
# Order consensus
Groups<-output$table.dend[,c(6,5)]
Groups<-Groups[order(Groups[,2],decreasing=TRUE),]
print(Groups)
## Identification of the codes with the numbers.
cbind(output$dendrogram$labels)
## To reproduce dendrogram
dend<-output$dendrogram
data<-output$table.dend
plot(dend)
text(data[,3],data[,4],data[,5])
# Other examples
# classical dendrogram
dend<-as.dendrogram(output$dendrogram)
plot(dend,type="r",edgePar = list(lty=1:2, col=2:1))
text(data[,3],data[,4],data[,5],col="blue",cex=1)
plot(dend,type="t",edgePar = list(lty=1:2, col=2:1))
text(data[,3],data[,4],data[,5],col="blue",cex=1)
## Without the control of duplicates
output<-consensus( pamCIP,duplicate=FALSE,nboot=5)
## using distance gower, require cluster package.
# output<-consensus( pamCIP,distance="gower", method="complete",nboot=5)

Data of corn

Description

Data from a completely randomized design where four different methods of growing corn resulted in various yields per acre on various plots of ground where the four methods were tried. Ordinarily, only one statistical analysis is used, but here we will use the kuskal-wallis test so that a rough comparison may be made with the mediasn test.

Usage

data(corn)

Format

A data frame with 34 observations on the following 3 variables.

method

a numeric vector

observation

a numeric vector

rx

a numeric vector

Details

The observations are ranked from the smallest, 77, of rank 1 to the largest 101, of rank N=34. Ties values receive the averarge rank.

Source

Book: Practical Nonparametric Statistics.

References

Practical Nonparametrics Statistics. W.J. Conover. Third Edition, 1999.

Examples

data(corn)
str(corn)

Correlation Coefficient

Description

An exact correlation for ties or without ties. Methods of Kendall, Spearman and Pearson.

Usage

correl(x, y, method = "pearson",alternative="two.sided")

Arguments

Value

The correlation of x,y vector with the statistical value and its probability

Author(s)

Felipe de Mendiburu

References

Numerical Recipes in C. Second Edition.

See Also

correlation

Examples

library(agricolae)
data(soil)
with(soil,correl(pH,clay,method="kendall"))
with(soil,correl(pH,clay,method="spearman"))
with(soil,correl(pH,clay,method="pearson"))

Correlation analysis. Methods of Pearson, Spearman, Kendall and Lin

Description

It obtains the coefficients of correlation and p-value between all the variables of a data table. The methods to apply are Pearson, Spearman , Kendall and lin's concordance index. In case of not specifying the method, the Pearson method will be used. The results are similar to SAS.

Usage

correlation(x,y=NULL, method = c("pearson", "kendall", "spearman", "lin")
,alternative="two.sided")

Arguments

Details

Parameters equal to function cor()

Value

The correlation matrix with its probability

Author(s)

Felipe de Mendiburu

References

Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989; 45, 255-268.

See Also

correl

Examples

library(agricolae)
data(soil)
# example 1
analysis<-correlation(soil[,2:8],method="pearson")
analysis
# Example 2: correlation between pH, variable 2 and other elements from soil.
analysis<-with(soil,correlation(pH,soil[,3:8],method="pearson",alternative="less"))
analysis
# Example 3: correlation between pH and clay method kendall.
with(soil,correlation(pH,clay,method="kendall", alternative="two.sided"))

Data of cotton

Description

Data of cotton collected in experiments of two localities in Lima and Pisco, Peru.

Usage

data(cotton)

Format

A data frame with 96 observations on the following 5 variables.

site

a factor with levels Lima Pisco

block

a factor with levels I II III IV V VI

lineage

a numeric vector

epoca

a numeric vector

yield

a numeric vector

Source

Book spanish: Metodos estadisticos para la investigacion. Autor: Calzada Benza Universidad Nacional Agraria - La Molina - Peru..

References

Book spanish: Metodos estadisticos para la investigacion. Autor: Calzada Benza Universidad Nacional Agraria - La Molina - Peru.

Examples

library(agricolae)
data(cotton)
str(cotton)

Coefficient of the experiment variation

Description

It obtains the coefficient of variation of the experiment obtained by models lm() or aov()

Usage

cv.model(x)

Arguments

Details

sqrt(MSerror)*100/mean(x)

Value

Returns the coefficient of variation of the experiment according to the applied statistical model

Author(s)

Felipe de Mendiburu

See Also

LSD.test, HSD.test, waller.test

Examples

# see examples from LSD , Waller-Duncan or HSD and complete with it:
library(agricolae)
# not run
# cv<-cv.model(model)

Coefficient of the similarity matrix variation

Description

This process consists of finding the coefficient of the distances of similarity of binary tables (1 and 0) as used for scoring molecular marker data for presence and absence of PCR amplification products.

Usage

cv.similarity(A)

Arguments

Value

Returns the coefficient of variation of the similarity model

Author(s)

Felipe de Mendiburu

See Also

similarity, resampling.cv

Examples

# molecular markers.
library(agricolae)
data(markers)
cv<-cv.similarity(markers)

Omitting the rows or columns with missing observations of a matrix (NA)

Description

In many situations it is required to omit the rows or columns less or greater with NA of the matrix.

Usage

delete.na(x, alternative=c("less", "greater") )

Arguments

Value

Author(s)

Felipe de Mendiburu

Examples

library(agricolae)
x<-c(2,5,3,7,5,NA,8,0,4,3,NA,NA)
dim(x)<-c(4,3)
x
#     [,1] [,2] [,3]
#[1,]    2    5    4
#[2,]    5   NA    3
#[3,]    3    8   NA
#[4,]    7    0   NA

delete.na(x,"less")
#     [,1]
#[1,]    2
#[2,]    5
#[3,]    3
#[4,]    7

delete.na(x,"greater")
#     [,1] [,2] [,3]
#[1,]    2    5    4

Design of experiments for a factorial

Description

It generates a design of blocks, randomize and latin square for combined n. factors uses the methods of number generation in R. The seed is by set.seed(seed, kinds).

Usage

design.ab(trt, r, serie = 2, design=c("rcbd","crd","lsd"),
seed = 0, kinds = "Super-Duper",first=TRUE,randomization=TRUE)

Arguments

Details

kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )

Value

Author(s)

Felipe de Mendiburu

References

Introduction to Experimental Statistics. Ching Chun Li. McGraw-Hill Book Company, INC, New. York, 1964

See Also

design.split, design.alpha,design.bib, design.crd , design.cyclic , design.dau , design.graeco, design.lattice, design.lsd, design.rcbd, design.strip

Examples

# factorial 3 x 2 with 3 blocks
library(agricolae)
trt<-c(3,2) # factorial 3x2
outdesign <-design.ab(trt, r=3, serie=2)
book<-outdesign$book
head(book,10) # print of the field book
# factorial 2 x 2 x 2 with 5 replications in completely randomized design.
trt<-c(2,2,2)
outdesign<-design.ab(trt, r=5, serie=2,design="crd")
book<-outdesign$book
print(book)
# factorial 3 x 3 in latin square design.
trt <-c(3,3)
outdesign<-design.ab(trt, serie=2, design="lsd")
book<-outdesign$book
print(book)

Alpha design type (0,1)

Description

Generates an alpha designs starting from the alpha design fixing under the series formulated by Patterson and Williams. These designs are generated by the alpha arrangements. They are similar to the lattice designs, but the tables are rectangular s by k (with s blocks and k<s columns. The number of treatments should be equal to s*k and all the experimental units r*s*k (r replications).

Usage

design.alpha(trt, k, r, serie = 2, seed = 0, kinds = "Super-Duper",randomization=TRUE)

Arguments

Details

Parameters for the alpha design: I. r=2, k <= s; II. r=3, s odd, k <= s; III.r=3, s even, k <= s-1; IV. r=4, s odd but not a multiple of 3, k<=s

r= replications s=number of blocks k=size of block Number of treatment is equal to k*s

Value

Author(s)

Felipe de Mendiburu

References

H.D. Patterson and E.R. Williams. Biometrika (1976) A new class of resolvable incomplete block designs. printed in Great Britain. Online: http://biomet.oxfordjournals.org/cgi/content/abstract/63/1/83

See Also

design.ab, design.split,design.bib, design.crd , design.cyclic , design.dau , design.graeco, design.lattice, design.lsd, design.rcbd, design.strip

Examples

library(agricolae)
#Example one
trt<-1:30
t <- length(trt)
# size block k
k<-3
# Blocks s
s<-t/k
# replications r
r <- 2
outdesign<- design.alpha(trt,k,r,serie=2)
book<-outdesign$book
plots<-book[,1]
dim(plots)<-c(k,s,r)
for (i in 1:r) print(t(plots[,,i]))
outdesign$sketch
# Example two 
trt<-letters[1:12] 
t <- length(trt)
k<-3
r<-3
s<-t/k
outdesign<- design.alpha(trt,k,r,serie=2)
book<-outdesign$book
plots<-book[,1]
dim(plots)<-c(k,s,r)
for (i in 1:r) print(t(plots[,,i]))
outdesign$sketch

Randomized Balanced Incomplete Block Designs. BIB

Description

Creates Randomized Balanced Incomplete Block Design. "Random" uses the methods of number generation in R. The seed is by set.seed(seed, kinds).

Usage

design.bib(trt, k, r=NULL, serie = 2, seed = 0, kinds = "Super-Duper",
maxRep=20,randomization=TRUE)

Arguments

Details

The package AlgDesign is necessary.

if r = NULL, then it calculates the value of r smaller for k defined. In the case of r = value, then the possible values for "r" is calculated

K is the smallest integer number of treatments and both values are consistent in design.

kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )

Value

Author(s)

Felipe de Mendiburu

References

1. Experimental design. Cochran and Cox. Second edition. Wiley Classics Library Edition published 1992

2. Optimal Experimental Design with R. Dieter Rasch, Jurgen Pilz, Rob Verdooren and Albrecht Gebhardt. 2011 by Taylor and Francis Group, LLC CRC Press is an imprint of Taylor and Francis Group, an Informa business.

3. Design of Experiments. Robert O. Kuehl. 2nd ed., Duxbury, 2000.

See Also

design.ab, design.alpha,design.split, design.crd , design.cyclic , design.dau , design.graeco, design.lattice, design.lsd, design.rcbd, design.strip

Examples

library(agricolae)
# 4 treatments and k=3 size block
trt<-c("A","B","C","D")
k<-3
outdesign<-design.bib(trt,k,serie=2,seed =41,kinds ="Super-Duper") # seed = 41
print(outdesign$parameters)
book<-outdesign$book
plots <-as.numeric(book[,1])
matrix(plots,byrow=TRUE,ncol=k)
print(outdesign$sketch)
# write in hard disk
# write.csv(book,"book.csv", row.names=FALSE)
# file.show("book.csv")

Completely Randomized Design

Description

It generates completely a randomized design with equal or different repetition. "Random" uses the methods of number generation in R. The seed is by set.seed(seed, kinds).

Usage

design.crd(trt, r, serie = 2, seed = 0, kinds = "Super-Duper",randomization=TRUE)

Arguments

Details

kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )

Value

Author(s)

Felipe de Mendiburu

References

Introduction to Experimental Statistics. Ching Chun Li. McGraw-Hill Book Company, INC, New. York, 1964

See Also

design.ab, design.alpha,design.bib, design.split , design.cyclic , design.dau , design.graeco, design.lattice, design.lsd, design.rcbd, design.strip

Examples

library(agricolae)
trt <-c("CIP-101","CIP-201","CIP-301","CIP-401","CIP-501")
r <-c(4,3,5,4,3)
# seed = 12543
outdesign1 <-design.crd(trt,r,serie=2,2543,"Mersenne-Twister")
book1<-outdesign1
# no seed
outdesign2 <-design.crd(trt,r,serie=3)
print(outdesign2$parameters)
book2<-outdesign2
# write to hard disk
# write.table(book1,"crd.txt", row.names=FALSE, sep="\t")
# file.show("crd.txt")

Cyclic designs

Description

The cyclic design is a incomplete blocks designs, it is generated from a incomplete block initial of the size k, the plan is generated and randomized. The efficient and robust cyclic designs for 6 to 30 treatments, replications <= 10.

Usage

design.cyclic(trt, k, r, serie = 2, rowcol = FALSE, seed = 0, kinds = "Super-Duper"
,randomization=TRUE)

Arguments

Details

Number o treatment 6 to 30. (r) Replication 2 to 10. (k) size of block 2 to 10. replication = i*k, "i" is value integer.

Value

Author(s)

Felipe de Mendiburu

References

Kuehl, Robert(2000), Design of Experiments. 2nd ed., Duxbury. John, J.A. (1981) Efficient Cyclic Design. J. R. Statist. Soc. B, 43, No. 1, pp, 76-80.

See Also

design.ab, design.alpha,design.bib, design.crd , design.split , design.dau , design.graeco, design.lattice, design.lsd, design.rcbd, design.strip

Examples

library(agricolae)
trt<-letters[1:8]
# block size = 2, replication = 6
outdesign1 <- design.cyclic(trt,k=2, r=6,serie=2)
names(outdesign1)
# groups 1,2,3
outdesign1$sketch[[1]]
outdesign1$sketch[[2]]
outdesign1$sketch[[3]]
outdesign1$book
# row-column design
outdesign2<- design.cyclic(trt,k=2, r=6, serie=2, rowcol=TRUE)
outdesign2$sketch

Augmented block design

Description

These are designs for two types of treatments: the control treatments (common) and the increased treatments. The common treatments are applied in complete randomized blocks, and the increased treatments, at random. Each treatment should be applied in any block once only. It is understood that the common treatments are of a greater interest; the standard error of the difference is much smaller than when between two increased ones in different blocks.

Usage

design.dau(trt1, trt2, r, serie = 2, seed = 0, kinds = "Super-Duper", name="trt"
,randomization=TRUE)

Arguments

Details

kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )

Value

Author(s)

Felipe de Mendiburu

References

1. Augmented (or Hoonuiaku) Design. Federer, W.T. (1956), Hawaii Plr. rec., 55: 191-208. 2. In Augmented Designs. Federer, W.T and Raghavarao, D. (1975). Bometrics, vol. 31, No. 1 (mar.., 1975), pp. 29-35

See Also

design.ab, design.alpha,design.bib, design.crd , design.cyclic , design.split , design.graeco, design.lattice, design.lsd, design.rcbd, design.strip

Examples

library(agricolae)
# 4 treatments and 5 blocks
T1<-c("A","B","C","D")
T2<-letters[20:26]
outdesign <-design.dau(T1,T2, r=5,serie=2)
# field book
book<-outdesign$book
by(book,book[2],function(x) paste(x[,1],"-",as.character(x[,3])))
# write in hard disk
# write.table(book,"dau.txt", row.names=FALSE, sep="\t")
# file.show("dau.txt")
# Augmented designs in Completely Randomized Design
trt<-c(T1,T2)
r<-c(4,4,4,4,1,1,1,1,1,1,1)
outdesign <- design.crd(trt,r)
outdesign$book

Graeco - latin square design

Description

A graeco - latin square is a KxK pattern that permits the study of k treatments simultaneously with three different blocking variables, each at k levels.

The function is only for squares of the odd numbers and even numbers (4, 8, 10 and 12)

Usage

design.graeco(trt1, trt2, serie = 2, seed = 0, kinds = "Super-Duper",randomization=TRUE)

Arguments

Details

kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )

Value

Author(s)

Felipe de Mendiburu

References

1. Statistics for Experimenters Design, Innovation, and Discovery Second Edition. George E. P. Box. Wiley-Interscience. 2005.

2. Experimental design. Cochran and Cox. Second edition. Wiley Classics Library Edition published 1992.

See Also

design.ab, design.alpha,design.bib, design.crd , design.cyclic , design.dau , design.split, design.lattice, design.lsd, design.rcbd, design.strip

Examples

library(agricolae)
T1<-c("a","b","c","d")
T2<-c("v","w","x","y")
outdesign <- design.graeco(T1,T2,serie=1)
graeco<-outdesign$book
plots <-as.numeric(graeco[,1])
print(outdesign$sketch)
print(matrix(plots,byrow=TRUE,ncol=4))
# 10 x 10
T1 <- letters[1:10]
T2 <- 1:10
outdesign <-  design.graeco(T1,T2,serie=2)
print(outdesign$sketch)

Lattice designs

Description

SIMPLE and TRIPLE lattice designs. It randomizes treatments in k x k lattice.

Usage

design.lattice(trt, r=3, serie = 2, seed = 0, kinds = "Super-Duper",randomization=TRUE)

Arguments

Details

kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )

Value

Author(s)

Felipe de Mendiburu

References

FIELD PLOT TECHNIQUE. Erwin L. LeCLERG. 2nd ed., 1962, Burgess Publishing Company, Minnesota

See Also

design.ab, design.alpha,design.bib, design.crd , design.cyclic , design.dau , design.graeco, design.split, design.lsd, design.rcbd, design.strip

Examples

library(agricolae)
# triple lattice
trt<-LETTERS[1:9]
outdesign<-design.lattice(trt,r=3,serie=2) # triple lattice design ( 9 trt)
# simple lattice
trt<-1:100
outdesign<-design.lattice(trt,r=2,serie=3) # simple lattice design, 10x10 

Latin Square Design

Description

It generates Latin Square Design. "Random" uses the methods of number generation in R. The seed is by set.seed(seed, kinds).

Usage

design.lsd(trt, serie = 2, seed = 0, kinds = "Super-Duper",first=TRUE,randomization=TRUE)

Arguments

Details

kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )

Value

Author(s)

Felipe de Mendiburu

References

Introduction to Experimental Statistics. Ching Chun Li. McGraw-Hill Book Company, INC, New. York, 1969

See Also

design.ab, design.alpha,design.bib, design.crd , design.cyclic , design.dau , design.graeco, design.lattice, design.split, design.rcbd, design.strip

Examples

library(agricolae)
varieties<-c("perricholi","yungay","maria bonita","tomasa")
outdesign <-design.lsd(varieties,serie=2,seed=23)
lsd <- outdesign$book 
print(outdesign$sketch)
print(lsd) # field book.
plots <-as.numeric(lsd[,1])
print(matrix(plots,byrow = TRUE, ncol = 4))
# Write on hard disk.
# write.table(lsd,"lsd.txt", row.names=FALSE, sep="\t")
# file.show("lsd.txt")

Experimental design matrix

Description

Generate the design matrix from the fieldbook generated by an experimental plan or a dataframe for analysis.

Usage

design.mat(book, locations)

Arguments

Value

X is matrix design.

Author(s)

Felipe de Mendiburu

See Also

design.ab, design.alpha,design.bib, design.crd , design.cyclic , design.split , design.graeco, design.lattice, design.lsd, design.rcbd, design.strip, design.dau

Examples

# dataframe: data analysis
library(agricolae)
data(sweetpotato)
X<-design.mat(sweetpotato,1)
print(X)
# fieldbook: RCBD design
trt <- LETTERS[1:4]
r<-3
plan<-design.rcbd(trt,r,seed=11)
X<-design.mat(plan$book,2:3)
print(X)

Randomized Complete Block Design

Description

It generates Randomized Complete Block Design. "Random" uses the methods of number generation in R. The seed is by set.seed(seed, kinds).

Usage

design.rcbd(trt, r, serie = 2, seed = 0, kinds = "Super-Duper", first=TRUE,
continue=FALSE,randomization=TRUE )

Arguments

Details

kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )

Value

Author(s)

Felipe de Mendiburu

References

Introduction to Experimental Statistics. Ching Chun Li. McGraw-Hill Book Company, INC, New. York, 1964

See Also

design.ab, design.alpha,design.bib, design.crd , design.cyclic , design.dau , design.graeco, design.lattice, design.lsd, design.split, design.strip

Examples

library(agricolae)
# 5 treatments and 6 blocks
trt<-c("A","B","C","D","E")
outdesign <-design.rcbd(trt,6,serie=2,986,"Wichmann-Hill") # seed = 986
book <-outdesign$book # field book
# write in hard disk
# write.table(book,"rcbd.txt", row.names=FALSE, sep="\t")
# file.show("rcbd.txt")
# Plots in field model ZIGZAG
fieldbook <- zigzag(outdesign)
print(outdesign$sketch)
print(matrix(fieldbook[,1],byrow=TRUE,ncol=5))
# continuous numbering of plot
outdesign <-design.rcbd(trt,6,serie=0,continue=TRUE)
head(outdesign$book)

Split Plot Design

Description

It generates split plot design. "Random" uses the methods of number generation in R. The seed is by set.seed(seed, kinds).

Usage

design.split(trt1, trt2,r=NULL, design=c("rcbd","crd","lsd"),serie = 2,
seed = 0, kinds = "Super-Duper", first=TRUE,randomization=TRUE)

Arguments

Details

kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )

Value

Author(s)

Felipe de Mendiburu

References

Statistical Procedures for Agricultural Research. Kwanchai A. Gomez, Arturo A. Gomez. John Wiley & Sons, new York, 1984

See Also

design.ab, design.alpha,design.bib, design.crd , design.cyclic , design.dau , design.graeco, design.lattice, design.lsd, design.rcbd, design.strip

Examples

library(agricolae)
# 4 treatments and 5 blocks in split-plot
t1<-c("A","B","C","D")
t2<-c(1,2,3)
outdesign <-design.split(t1,t2,r=3,serie=2,seed=45,kinds ="Super-Duper")#seed=45
book<-outdesign$book# field book
# write in hard disk
# write.table(book,"book.txt", row.names=FALSE, sep="\t")
# file.show("book.txt")

Strip Plot Design

Description

It generates strip plot design. "Random" uses the methods of number generation in R. The seed is by set.seed(seed, kinds).

Usage

design.strip(trt1, trt2,r, serie = 2, seed = 0, kinds = "Super-Duper",randomization=TRUE)

Arguments

Details

kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )

Value

Author(s)

Felipe de Mendiburu

References

Statistical Procedures for Agricultural Research. Kwanchai A. Gomez, Arturo A. Gomez. John Wiley & Sons, new York, 1984

See Also

design.ab, design.alpha,design.bib, design.crd , design.cyclic , design.dau , design.graeco, design.lattice, design.lsd, design.rcbd, design.split

Examples

library(agricolae)
# 4 and 3 treatments and 3 blocks in strip-plot
t1<-c("A","B","C","D")
t2<-c(1,2,3)
r<-3
outdesign <-design.strip(t1,t2,r, serie=2,seed=45,kinds ="Super-Duper") # seed = 45
book <-outdesign$book # field book
# write in hard disk
# write.table(book,"book.txt", row.names=FALSE, sep="\t")
# file.show("book.txt")

Incomplete Latin Square Design

Description

Such designs are referred to as Youden squares since they were introduced by Youden (1937) after Yates (1936) considered the special case of column equal to number treatment minus 1. "Random" uses the methods of number generation in R. The seed is by set.seed(seed, kinds).

Usage

design.youden(trt, r, serie = 2, seed = 0, kinds = "Super-Duper",first=TRUE
,randomization=TRUE)

Arguments

Details

kinds <- c("Wichmann-Hill", "Marsaglia-Multicarry", "Super-Duper", "Mersenne-Twister", "Knuth-TAOCP", "user-supplied", "Knuth-TAOCP-2002", "default" )

Value

Author(s)

Felipe de Mendiburu

References

Design and Analysis of experiment. Hinkelmann, Klaus and Kempthorne, Oscar. Wiley-Interscience. Copyright (2008) by John Wiley and Sons. Inc., Hoboken, new Yersy

See Also

design.ab, design.alpha,design.bib, design.crd , design.cyclic , design.dau , design.graeco, design.lattice, design.split, design.rcbd, design.strip, design.lsd

Examples

library(agricolae)
varieties<-c("perricholi","yungay","maria bonita","tomasa")
r<-3
outdesign <-design.youden(varieties,r,serie=2,seed=23)
youden <- outdesign$book
print(outdesign$sketch)
plots <-as.numeric(youden[,1])
print(matrix(plots,byrow=TRUE,ncol=r))
print(youden) # field book.
# Write on hard disk.
# write.table(youden,"youden.txt", row.names=FALSE, sep="\t")
# file.show("youden.txt")

Plotting the multiple comparison of means

Description

It plots bars of the averages of treatments to compare. It uses the objects generated by a procedure of comparison like LSD (Fisher), duncan, Tukey (HSD), Student Newman Keul (SNK), Scheffe, Ryan, Einot and Gabriel and Welsch (REGW), Kruskal Wallis, Friedman and Waerden.

Usage

diffograph(x, main=NULL,color1="red",color2="blue",color3="black",
cex.axis=0.8,las=1,pch=20,bty="l",cex=0.8,lwd=1,xlab="",ylab="",...)

Arguments

Details

The graph.diff function should be used for functions: LSD, duncan, SNK, scheffe, REGW, HSD, kruskal, friedman and waerden test.

Value

Author(s)

Felipe de Mendiburu

References

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC

See Also

LSD.test, HSD.test, duncan.test, SNK.test, scheffe.test, REGW.test, kruskal,friedman, waerden.test

Examples

# Example 1
library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
x<- LSD.test(model,"virus",alpha=0.01,group=FALSE)
diffograph(x,cex.axis=0.8,xlab="Yield",ylab="")
# Example 2
x<- REGW.test(model,"virus",alpha=0.01,group=FALSE)
diffograph(x,cex.axis=0.6,xlab="Yield",ylab="",color1="brown",color2="green")

Data evaluation of the disease overtime

Description

Three evaluations over time and the potato yield when applying several treatments.

Usage

data(disease)

Format

A data frame with 21 observations on the following 7 variables.

plots

a numeric vector

rep

a numeric vector

trt

a factor with levels T0 T1 T2 T3 T4 T5 T6

E2

a numeric vector

E5

a numeric vector

E7

a numeric vector

yield

a numeric vector

Source

Experimental data.

References

International Potato Center. CIP - Lima Peru.

Examples

library(agricolae)
data(disease)
str(disease)

Duncan's new multiple range test

Description

This test is adapted from the Newman-Keuls method. Duncan's test does not control family wise error rate at the specified alpha level. It has more power than the other post tests, but only because it doesn't control the error rate properly. The Experimentwise Error Rate at: 1-(1-alpha)^(a-1); where "a" is the number of means and is the Per-Comparison Error Rate. Duncan's procedure is only very slightly more conservative than LSD. The level by alpha default is 0.05.

Usage

duncan.test(y, trt, DFerror, MSerror, alpha = 0.05, group=TRUE, main = NULL,console=FALSE)

Arguments

Details

It is necessary first makes a analysis of variance.

if y = model, then to apply the instruction:
duncan.test(model, "trt", alpha = 0.05, group = TRUE, main = NULL, console = FALSE)
where the model class is aov or lm.

Value

Author(s)

Felipe de Mendiburu

References

1. Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third Edition 1997
2. Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

See Also

BIB.test, DAU.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
out <- duncan.test(model,"virus", 
main="Yield of sweetpotato. Dealt with different virus")
plot(out,variation="IQR")
duncan.test(model,"virus",alpha=0.01,console=TRUE)
# version old duncan.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
out <- with(sweetpotato,duncan.test(yield,virus,df,MSerror, group=TRUE))
plot(out,horiz=TRUE,las=1)
print(out$groups)

Durbin test and multiple comparison of treatments

Description

A multiple comparison of the Durbin test for the balanced incomplete blocks for sensorial or categorical evaluation. It forms groups according to the demanded ones for level of significance (alpha); by default, 0.05.

Usage

durbin.test(judge, trt, evaluation, alpha = 0.05, group =TRUE, 
main = NULL, console=FALSE)

Arguments

Details

The post hoc test is using the criterium Fisher's least significant difference.

Value

Author(s)

Felipe de Mendiburu

References

Practical Nonparametrics Statistics. W.J. Conover, 1999 Nonparametric Statistical Methods. Myles Hollander and Douglas A. Wofe, 1999

See Also

BIB.test, DAU.test, duncan.test, friedman, HSD.test, kruskal, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
# Example 1. Conover, pag 391
person<-gl(7,3)
variety<-c(1,2,4,2,3,5,3,4,6,4,5,7,1,5,6,2,6,7,1,3,7)
preference<-c(2,3,1,3,1,2,2,1,3,1,2,3,3,1,2,3,1,2,3,1,2)
out<-durbin.test(person,variety,preference,group=TRUE,console=TRUE,
main="Seven varieties of ice cream manufacturer")
#startgraph
bar.group(out$groups,horiz=TRUE,xlim=c(0,10),density=4,las=1)
#endgraph
# Example 2. Myles Hollander, pag 311
# Source: W. Moore and C.I. Bliss. 1942
day<-gl(7,3)
chemical<-c("A","B","D","A","C","E","C","D","G","A","F","G","B","C","F",
 "B","E","G","D","E","F")
toxic<-c(0.465,0.343,0.396,0.602,0.873,0.634,0.875,0.325,0.330,0.423,0.987,
0.426,0.652,1.142,0.989,0.536,0.409,0.309,0.609,0.417,0.931)
out<-durbin.test(day,chemical,toxic,group=TRUE,console=TRUE,
main="Logarithm of Toxic Dosages")
plot(out)

Friedman test and multiple comparison of treatments

Description

The data consist of b-blocks mutually independent k-variate random variables Xij, i=1,..,b; j=1,..k. The random variable X is in block i and is associated with treatment j. It makes the multiple comparison of the Friedman test with or without ties. A first result is obtained by friedman.test of R.

Usage

friedman(judge,trt,evaluation,alpha=0.05,group=TRUE,main=NULL,console=FALSE)

Arguments

Details

The post hoc friedman test is using the criterium Fisher's least significant difference (LSD)

Value

Author(s)

Felipe de Mendiburu

References

Practical Nonparametrics Statistics. W.J. Conover, 1999

See Also

BIB.test, DAU.test, duncan.test, durbin.test, HSD.test, kruskal, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
data(grass)
out<-with(grass,friedman(judge,trt, evaluation,alpha=0.05, group=TRUE,console=TRUE,
main="Data of the book of Conover"))
#startgraph
plot(out,variation="IQR")
#endgraph

Data of frijol

Description

Data of frijol under 4 technologies for the homogeneity of regression study. Yield of Frijol in kg/ha in clean and dry grain.

Tecnnologies: 20-40-20 kg/ha. N. P2O5 and K2O + 2 t/ha of gallinaza. 40-80-40 kg/ha. N. P2O5 and K2O + 2 t/ha of gallinaza. 60-120-60 kg/ha. N. P2O5 and K2O + 2 t/ha of gallinaza. 40-80-40 kg/ha. N. P2O5 and K2O + 4 t/ha of gallinaza.

Usage

data(frijol)

Format

A data frame with 84 observations on the following 3 variables.

technology

a factor with levels a b c d

production

a numeric vector

index

a numeric vector

References

Oriente antioqueno (1972) (ICA.- Orlando Martinez W.) Colombia.

Examples

library(agricolae)
data(frijol)
str(frijol)

Data of potato yield in a different environment

Description

50 genotypes and 5 environments.

Usage

data(genxenv)

Format

A data frame with 250 observations on the following 3 variables.

ENV

a numeric vector

GEN

a numeric vector

YLD

a numeric vector

Source

International Potato Center. CIP - Lima Peru.

References

International Potato Center. CIP - Lima Peru.

Examples

library(agricolae)
data(genxenv)
str(genxenv)

Histogram

Description

In many situations it has intervals of class defined with its respective frequencies. By means of this function, the graphic of frequency is obtained and it is possible to superpose the normal distribution, polygon of frequency, Ojiva and to construct the table of complete frequency.

Usage

graph.freq(x, breaks=NULL,counts=NULL,frequency=1, plot=TRUE, nclass=NULL,
xlab="",ylab="",axes = "",las=1,...)

Arguments

Value

Author(s)

Felipe de Mendiburu

See Also

polygon.freq, table.freq, stat.freq,inter.freq,sturges.freq, join.freq,ogive.freq, normal.freq

Examples

library(agricolae)
data(genxenv)
yield <- subset(genxenv$YLD,genxenv$ENV==2)
yield <- round(yield,1)
h<- graph.freq(yield,axes=FALSE, frequency=1, ylab="frequency",col="yellow")
axis(1,h$breaks)
axis(2,seq(0,20,0.1))
# To reproduce histogram.
h1 <- graph.freq(h, col="blue", frequency=2,border="red", density=8,axes=FALSE,
xlab="YIELD",ylab="relative")
axis(1,h$breaks)
axis(2,seq(0,.4,0.1))
# summary, only frecuency
limits <-seq(10,40,5)
frequencies <-c(2,6,8,7,3,4)
#startgraph
h<-graph.freq(limits,counts=frequencies,col="bisque",xlab="Classes")
polygon.freq(h,col="red")
title( main="Histogram and polygon of frequency",
ylab="frequency")
#endgraph
# Statistics
measures<-stat.freq(h)
print(measures)
# frequency table full
round(table.freq(h),2)
#startgraph
# ogive
ogive.freq(h,col="red",type="b",ylab="Accumulated relative frequency",
xlab="Variable")
# only .frequency polygon
h<-graph.freq(limits,counts=frequencies,border=FALSE,col=NULL,xlab="  ",ylab="")
title( main="Polygon of frequency",
xlab="Variable", ylab="Frecuency")
polygon.freq(h,col="blue")
grid(col="brown")
#endgraph
# Draw curve for Histogram
h<- graph.freq(yield,axes=FALSE, frequency=3, ylab="f(yield)",col="yellow")
axis(1,h$breaks)
axis(2,seq(0,0.18,0.03),las=2)
lines(density(yield), col = "red", lwd = 2)
title("Draw curve for Histogram")

Data for Friedman test

Description

Twelve homeowners are selected randomly to participate in an experiment with a plant nursery. Each homeowner is asked to select four fairly identical areas in his yard and to plant four different types of grasses, one in each area.

Usage

data(grass)

Format

A data frame with 48 observations on the following 3 variables.

judge

a numeric vector

trt

a factor with levels t1 t2 t3 t4

evaluation

a numeric vector

Details

Each of the 12 blocks consists of four fairly identical plots of land, each receiving care of approximately the same degree of skill because the four plots are presumably cared for by the same homeowern.

Source

Book: Practical Nonparametrics Statistics, pag 372.

References

Practical Nonparametrics Statistics. W.J. Conover, 1999

Examples

data(grass)
str(grass)

Data in greenhouse

Description

Potato minituber production in greenhouse, three sets of data in potato varieties with different methods: hydroponics, Aeroponic, Pots and Plant beds, the unit is in grams and the number of tubers in units,

Usage

data(greenhouse)

Details

greenhouse is list, three tables: greenhouse1(480 obs, 5 var), yield for plant, unit is grams. greenhouse2(48 obs, 5 var), Yields of 10 plants by experimental unit(grams). planting date(April 24, 2004) and harvest date(July 16, 2004) and greenhouse3(480 obs, 5 var), Tubers by plants.

Source

International Potato Center(CIP). Lima-Peru. Data Kindly provided by Carlos Chuquillanqui.

References

- Produccion de semila de papa por hidroponia tecnica de flujo continuo de una pelicula de solucion nutritiva (nft) Carlos Chuquillanqui(CIP), Jorge Tenorio(CIP) and L. F. Salazar(Agdia Inc). AGROENFOQUE Lima-Peru (2004) - Potato Minituber Production Using Aeroponics: Effect of Plant Density and Harvesting Intervals American Journal of Potato Research, Jan/Feb 2006 by Farran, Imma, Mingo-Castel, Angel M

Examples

library(agricolae)
data(greenhouse)
greenhouse1 <- greenhouse$greenhouse1
greenhouse2 <- greenhouse$greenhouse2
greenhouse3 <- greenhouse$greenhouse3

Data growth of trees

Description

Data growth of pijuayo trees in several localities.

Usage

data(growth)

Format

A data frame with 30 observations on the following 3 variables.

place

a factor with levels L1 L2

slime

a numeric vector

height

a numeric vector

Source

Experimental data (Pucallpa - Peru)

References

ICRAF lima Peru.

Examples

library(agricolae)
data(growth)
str(growth)

Data of AUDPC for nonparametrical stability analysis

Description

Published data. Haynes. Mean area under the disease progress curve (AUDPC) for each of 16 potato clones evaluated at eight sites across the United States in 1996

Usage

data(haynes)

Format

A data frame with 16 observations on the following 9 variables.

clone

a factor with levels A84118-3 AO80432-1 AO84275-3 AWN86514-2 B0692-4 B0718-3 B0749-2F B0767-2 Bertita Bzura C0083008-1 Elba Greta Krantz Libertas Stobrawa

FL

a numeric vector

MI

a numeric vector

ME

a numeric vector

MN

a numeric vector

ND

a numeric vector

NY

a numeric vector

PA

a numeric vector

WI

a numeric vector

References

Haynes K G, Lambert D H, Christ B J, Weingartner D P, Douches D S, Backlund J E, Fry W and Stevenson W. 1998. Phenotypic stability of resistance to late blight in potato clones evaluated at eight sites in the United States American Journal Potato Research 75, pag 211-217.

Examples

library(agricolae)
data(haynes)
str(haynes)

Cut tree of consensus

Description

It shows dendrogram of a consensus of a tree generated by hclust.

Usage

hcut(consensus,h,group,col.text="blue",cex.text=1,...)

Arguments

Value

hcut Returns a data frame with group memberships and consensus tree.

Author(s)

F. de Mendiburu

See Also

hclust, consensus, hgroups

Examples

library(agricolae)
data(pamCIP)
# only code
rownames(pamCIP)<-substr(rownames(pamCIP),1,6)
# groups of clusters
# output<-consensus(pamCIP,nboot=100)
# hcut(output,h=0.4,group=5,main="Group 5")
# 
# hcut(output,h=0.4,group=8,type="t",edgePar=list(lty=1:2,col=2:1),main="group 8"
# ,col.text="blue",cex.text=1)

Data of potato, Heterosis

Description

Determination of heterosis, general combining ability (GCA) and specific combining ability in tuber dry matter, reducing sugars and agronomic characteristics in TPS families.

Usage

data(heterosis)

Format

A data frame with 216 observations on the following 11 variables.

Place

1: La Molina, 2=Huancayo

Replication

a numeric vector

Treatment

a numeric vector

Factor

a factor with levels Control progenie progenitor testigo

Female

a factor with levels Achirana LT-8 MF-I MF-II Serrana TPS-2 TPS-25 TPS-7

Male

a factor with levels TPS-13 TPS-67 TS-15

v1

Yield (Kg/plant)

v2

Reducing sugars (scale):1 low and 5=High

v3

Tuber dry matter (percentage)

v4

Tuber number/plant

v5

Average tuber weight (g)

Details

The study was conducted in 3 environments, La Molina-PERU to 240 masl. during autumn-winter and spring, and in Huancayo-PERU 3180 masl., during summer. The experimental material consisted of 24 families half brother in the form of tubers derived from TPS, obtained crossing between 8 female and 3 male parents. Design used was randomized complete block with three repetitions. The experimental unit was 30 plants in two rows at a distance of 30cm between plants and 90 cm between rows. Variables evaluated were Yield, Tubers number, Dry matter and content and reducing sugars. The analysis was conducted line x tester. The control variety was Desiree.

Source

International Potato Center(CIP). Lima-Peru. Data Kindly provided by of Rolando Cabello.

References

Tesis "Heterosis, habilidad combinatoria general y especifica para materia seca, azucares reductores y caracteres agronomicos en familias de tuberculos provenientes de semilla sexual de papa. Magister Scientiae Rodolfo Valdivia Lorente. Universidad Nacional Agraria La molina-Lima Peru, Escuela de Post Grado, Mejoramiento genetico de plantas, 2004". Poster: Congreso de la Sociedad Peruana de Genetica - Peru, 2008.

Examples

library(agricolae)
data(heterosis)
str(heterosis)
site1<-subset(heterosis,heterosis[,1]==1)
site2<-subset(heterosis,heterosis[,1]==2)
site3<-subset(heterosis,heterosis[,1]==3)
model1<-with(site1,lineXtester(Replication, Female, Male, v1))
DFe <- df.residual(model1)
CMe <- deviance(model1)/DFe
test1 <- with(site1,HSD.test(v1, Factor,DFe,CMe))
test2 <- with(site1,HSD.test(v1, Treatment,DFe,CMe))
model22<-with(site2,lineXtester(Replication, Female, Male, v3))
model3<-with(site3,lineXtester(Replication, Female, Male, v4))

groups of hclust

Description

Returns a vector with group memberships. This function is used by the function consensus of clusters.

Usage

hgroups(hmerge)

Arguments

Value

The merge clusters is printed.

Author(s)

F. de Mendiburu

See Also

hclust, hcut, consensus

Examples

library(agricolae)
data(pamCIP)
# only code
rownames(pamCIP)<-substr(rownames(pamCIP),1,6)
distance <- dist(pamCIP,method="binary")
clusters<- hclust( distance, method="complete")
# groups of clusters
hgroups(clusters$merge)

Data: Rainfall thresholds as support for timing fungicide applications in the control of potato late blight in Peru

Description

Timing fungicide sprays based on accumulated rainfall thresholds can be a successful component of integrated management packages that include cultivars with moderate or high levels of resistance to late blight. The simplicity of measuring accumulated rainfall means that the technology can potentially be used by resource-poor farmers in developing countries.

Usage

data(huasahuasi)

Format

The format is: List of 2 ( AUDPC, YIELD )

block

a factor with levels I II III

trt

a factor with levels 40mm 7-days Non-application

clon

a factor with levels C386209.10 C387164.4 Cruza148 Musuq Yungay

y1da

a numeric vector, Kgr./plot

y2da

a numeric vector, Kgr./plot

y3ra

a numeric vector, Kgr./plot

d44

a numeric vector, 44 days

d51

a numeric vector, 51 days

d100

a numeric vector, 100 days

Details

The experimental unit was formed by 4 furrows of 1.8 m of length, with distance between furrows from 0.90 m and between plants of 0.30 m. In each furrow was installed 5 plants. The experiment had 3 repetitions. From the beginning of the experiment were fulfilled the following treatments Thresholds 40 mm: Apply the fungicide when 40 precipitation mm accumulates. The minimum interval between applications will be of 7 days. Schedule 7 days: The applications should be carried out every 7 days calendar. Without application: No fungicide application will be made. The evaluation of the severity of the late blight in each treatment started to emergency 80 percentage and then evaluations were made every 7 days until being observed a physiological maturation of the crop.

Source

Experimental field, 2003. Data Kindly provided by Wilmer Perez.

References

International Potato Center. CIP - Lima Peru.

Examples

library(agricolae)
data(huasahuasi)
names(huasahuasi)
str(huasahuasi$AUDPC)
str(huasahuasi$YIELD)

AMMI index and yield stability

Description

calculate AMMI stability value (ASV) and Yield stability index (YSI).

Usage

index.AMMI(model)

Arguments

Details

AMMI stability value (ASV) was calculated using the following formula, as suggested by Purchase (1997)

ASV = sqrt((SSpc1/SSpc2 * PC1i)^2+(PC2i)^2)

YSI = RASV + RY

RASV = rank(ASV) and RY = rank(Y across by environment)

Value

Author(s)

F. de Mendiburu

References

The use of an AMMI model and its parameters to analyse yield stability in multienvironment trials. N. SABAGHNIA, S.H. SABAGHPOUR AND H. DEHGHANI. Journal of Agricultural Science (2008), 146, 571-581. f 2008 Cambridge University Press 571 doi:10.1017/S0021859608007831 Printed in the United Kingdom

Parametric analysis to describe genotype x environment interaction and yield stability in winter wheat. PURCHASE, J. L. (1997). Ph.D. Thesis, Department of Agronomy, Faculty of Agriculture of the University of the Free State, Bloemfontein, South Africa.

See Also

AMMI,plot.AMMI

Examples

library(agricolae)
# Index AMMI
data(plrv)
model<- with(plrv,AMMI(Locality, Genotype, Rep, Yield, console=FALSE))
Idx<-index.AMMI(model)
names(Idx)
# Crops with improved stability according AMMI.
print(Idx[order(Idx[,3]),])
# Crops with better response and improved stability according AMMI.
print(Idx[order(Idx[,4]),])

Biodiversity Index

Description

Scientists use a formula called the biodiversity index to describe the amount of species diversity in a given area.

Usage

index.bio(data, method = c("Margalef", "Simpson.Dom", "Simpson.Div", 
"Berger.Parker", "McIntosh", "Shannon"), level=95, nboot=100, console=TRUE)

Arguments

Details

method bio-diversity. "Margalef" "Simpson.Dom" "Simpson.Div" "Berger.Parker" "McIntosh" "Shannon"

Value

Index and confidence intervals.

Author(s)

Felipe de Mendiburu

References

Magurran, A.E. (1988) Ecological diversity and its measurement. Princeton University Press Efron, B., Tibshirani, R. (1993) An Introduction to the Boostrap. Chapman and Hall/CRC

Examples

library(agricolae)
data(paracsho)
# date 22-06-05 and treatment CON = application with insecticide
specimens <- paracsho[1:10,6]
output1 <- index.bio(specimens,method="Simpson.Div",level=95,nboot=100)
output2 <- index.bio(specimens,method="Shannon",level=95,nboot=100)
rbind(output1, output2)

Uniformity soil. Smith's Index of Soil Heterogeneity

Description

Smith's index of soil heterogeneity is used primarily to derive optimum plot size. The index gives a single value as a quantitative measure of soil heterogeneity in an area. Graph CV for plot size and shape.

Usage

index.smith(data, PLOT=TRUE,...)

Arguments

Details

Vx=V(x)/x b

V(x) is the between-plot variance, Vx is the variance per unit area for plot size of x basic unit, and b is the Smith' index of soil heterogeneity.

Value

Author(s)

Felipe de Mendiburu

References

Statistical Procedures for Agriculture Research. Second Edition. Kwanchai A. Gomez and Arturo A. Gomez. 1976. USA

Examples

library(agricolae)
data(rice)
#startgraph
table<-index.smith(rice,
 main="Relationship between CV per unit area and plot size",col="red")
#endgraph
uniformity <- data.frame(table$uniformity)
uniformity
# regression variance per unit area an plot size.
model <- lm(Vx ~ I(log(Size)),uniformity)
coeff <- coef(model)
x<-1:max(uniformity$Size)
Vx<- coeff[1]+coeff[2]*log(x)
#startgraph
plot(x,Vx, type="l", col="blue",
 main="Relationship between variance per unit area and plot size")
points(uniformity$Size,uniformity$Vx)
#endgraph

Class intervals

Description

List class intervals.

Usage

inter.freq(x)

Arguments

Value

It show interval classes.

Author(s)

Felipe de Mendiburu

See Also

polygon.freq, table.freq, stat.freq, graph.freq, sturges.freq, join.freq, ogive.freq, normal.freq

Examples

library(agricolae)
# example 1
data(growth)
h<-hist(growth$height,plot=FALSE)
inter.freq(h)
# example 2
x<-seq(10,40,5)
y<-c(2,6,8,7,3,4)
inter.freq(x)
histogram <- graph.freq(x,counts=y)

Join class for histogram

Description

In many situations it is required to join classes because of the low .frequency in the intervals. In this process, it is required to join the intervals and ad the .frequencies of them.

Usage

join.freq(histogram, join)

Arguments

Value

New histogram with union of classes.

Author(s)

Felipe de Mendiburu

See Also

polygon.freq, table.freq, stat.freq, inter.freq, sturges.freq, graph.freq, ogive.freq, normal.freq

Examples

library(agricolae)
data(natives)
# histogram
h1<-graph.freq(natives$size,plot=FALSE)
round(table.freq(h1),4)
# Join classes  9, 10,11 and 12 with little frequency.
h2<-join.freq(h1,9:12)
# new table
plot(h2,col="bisque",xlab="Size")
round(summary(h2),4)

Correlation of Kendall

Description

Correlation of Kendall two set. Compute exact p-value with ties.

Usage

kendall(data1, data2)

Arguments

Value

The correlation of data1, data2 vector with the statistical value and its probability

Author(s)

Felipe de Mendiburu

References

Numerical Recipes in C. Second Edition. Pag 634

See Also

correlation

Examples

library(agricolae)
x <-c(1,1,1,4,2,2,3,1,3,2,1,1,2,3,2,1,1,2,1,2)
y <-c(1,1,2,3,4,4,2,1,2,3,1,1,3,4,2,1,1,3,1,2)
kendall(x,y)

Kruskal Wallis test and multiple comparison of treatments.

Description

It makes the multiple comparison with Kruskal-Wallis. The alpha parameter by default is 0.05. Post hoc test is using the criterium Fisher's least significant difference. The adjustment methods include the Bonferroni correction and others.

Usage

kruskal(y, trt, alpha = 0.05, p.adj=c("none","holm","hommel", 
"hochberg", "bonferroni", "BH", "BY", "fdr"), group=TRUE, main = NULL,console=FALSE)

Arguments

Details

For equal or different repetition.
For the adjustment methods, see the function p.adjusted.
p-adj = "none" is t-student.

Value

Author(s)

Felipe de Mendiburu

References

Practical Nonparametrics Statistics. W.J. Conover, 1999

See Also

BIB.test, DAU.test, duncan.test, durbin.test, friedman, HSD.test, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
data(corn)
str(corn)
comparison<-with(corn,kruskal(observation,method,group=TRUE, main="corn"))
comparison<-with(corn,kruskal(observation,method,p.adj="bon",group=FALSE, main="corn"))

Finding the Kurtosis coefficient

Description

It obtains the value of the kurtosis for a normally distributed variable. The result is similar to SAS.

Usage

kurtosis(x)

Arguments

Value

See Also

skewness

Examples

library(agricolae)
x<-c(3,4,5,2,3,4,5,6,4,NA,7)
kurtosis(x)
# value is -0.1517996

Setting the last character of a chain

Description

A special function for the group of treatments in the multiple comparison tests. Use plot.group.

Usage

lastC(x)

Arguments

Value

Author(s)

Felipe de Mendiburu

See Also

plot.group

Examples

library(agricolae)
x<-c("a","ab","b","c","cd")
lastC(x)
# "a" "b" "b" "c" "d"

LATEBLIGHT - Simulator for potato late blight Version LB2004

Description

LATEBLIGHT is a mathematical model that simulates the effect of weather, host growth and resistance, and fungicide use on asexual development and growth of Phytophthora infestans on potato foliage.

Usage

lateblight(WS, Cultivar, ApplSys,InocDate, LGR, IniSpor, SR, IE, LP, InMicCol,
MatTime=c('EARLYSEASON','MIDSEASON','LATESEASON'),...)

Arguments

Details

LATEBLIGHT Version LB2004 was created in October 2004 (Andrade-Piedra et al., 2005a, b and c), based on the C-version written by B.E. Ticknor ('BET 21191 modification of cbm8d29.c'), reported by Doster et al. (1990) and described in detail by Fry et al. (1991) (This version is referred as LB1990 by Andrade-Piedra et al. [2005a]). The first version of LATEBLIGHT was developed by Bruhn and Fry (1981) and described in detail by Bruhn et al. (1980).

Value

Note

All format data for date is yyyy-mm,dd, for example "2000-04-22". change with function as.Date()

Author(s)

Jorge L. Andrade-Piedra (1) (j.andrade@cgar.org), Gregory A. Forbes (1) (g.forbes@cgiar.org), Robert J. Hijmans (2) (rhijmans@ucdavis.edu), William E. Fry (3) (wef1@cornell.edu) Translation from C language into SAS language: G.A. Forbes Modifications: J.L. Andrade-Piedra and R.J. Hijmans Translation from SAS into R: Felipe de Mendiburu (1) (1) International Potato Center, P.O. Box 1558, Lima 12, Peru (2) University of California, One Shields Avenue, Davis, California 95616, USA (3) Cornell University, 351 Plant Science, Ithaca, NY 14853, USA

References

Andrade-Piedra, J. L., Hijmans, R. J., Forbes, G. A., Fry, W. E., and Nelson, R. J. 2005a. Simulation of potato late blight in the Andes. I: Modification and parameterization of the LATEBLIGHT model. Phytopathology 95:1191-1199.
Andrade-Piedra, J. L., Hijmans, R. J., Juarez, H. S., Forbes, G. A., Shtienberg, D., and Fry, W. E. 2005b. Simulation of potato late blight in the Andes. II: Validation of the LATEBLIGHT model. Phytopathology 95:1200-1208.
Andrade-Piedra, J. L., Forbes, G. A., Shtienberg, D., Grunwald, N. J., Chacon, M. G., Taipe, M. V., Hijmans, R. J., and Fry, W. E. 2005c. Qualification of a plant disease simulation model: Performance of the LATEBLIGHT model across a broad range of environments. Phytopathology 95:1412-1422.
Bruhn, J.A., Bruck, R.I., Fry, W.E., Arneson, P.A., and Keokosky, E.V. 1980. User's manual for LATEBLIGHT: a plant disease management game. Cornell University, Department of Plant Pathology, Ithaca, NY, USA. Mimeo 80-1.
Bruhn, J.A., and Fry, W.E. 1981. Analysis of potato late blight epidemiology by simulation modeling. Phytopathology 71:612-616.
Doster, M. A., Milgroom, M. G., and Fry, W. E. 1990. Quantification of factors influencing potato late blight suppression and selection for metalaxyl resistance in Phytophthora infestans - A simulation approach. Phytopathology 80:1190-1198.
Fry, W.E., Milgroom, M.G., Doster, M.A., Bruhn, J.A., and Bruck, R.I. 1991. LATEBLIGHT: a plant disease management game - User Manual. Version 3.1. Microsoft Windows Adaptation by B. E. Ticknor, and P. A. Arneson. Ithaca, Cornell University, Department of Plant Pathology, Ithaca, NY, USA.

See Also

weatherSeverity

Examples

library(agricolae)
f <- system.file("external/weather.csv", package="agricolae")
weather <- read.csv(f,header=FALSE)
f <- system.file("external/severity.csv", package="agricolae")
severity <- read.csv(f)
weather[,1]<-as.Date(weather[,1],format = "%m/%d/%Y")
# Parameters dates
dates<-c("2000-03-25","2000-04-09","2000-04-12","2000-04-16","2000-04-22")
dates<-as.Date(dates)
EmergDate <- as.Date('2000/01/19')
EndEpidDate <- as.Date("2000-04-22")
dates<-as.Date(dates)
NoReadingsH<- 1
RHthreshold <- 90
WS<-weatherSeverity(weather,severity,dates,EmergDate,EndEpidDate,
NoReadingsH,RHthreshold)
# Parameters Lateblight
InocDate<-"2000-03-18"
LGR <- 0.00410
IniSpor <- 0
SR <- 292000000
IE <- 1.0
LP <- 2.82
InMicCol <- 9
Cultivar <- 'NICOLA'
ApplSys <- "NOFUNGICIDE"
main<-"Cultivar: NICOLA"
#--------------------------
model<-lateblight(WS, Cultivar,ApplSys, InocDate, LGR,IniSpor,SR,IE, LP,
MatTime='LATESEASON',InMicCol,main=main,type="l",xlim=c(65,95),lwd=1.5,
xlab="Time (days after emergence)", ylab="Severity (Percentage)")
# reproduce graph
x<- model$Ofile$nday
y<- model$Ofile$SimSeverity
w<- model$Gfile$nday
z<- model$Gfile$MeanSeverity
Min<-model$Gfile$MinObs
Max<-model$Gfile$MaxObs
plot(x,y,type="l",xlim=c(65,95),lwd=1.5,xlab="Time (days after emergence)",
ylab="Severity (Percentage)")
points(w,z,col="blue",cex=1,pch=19)
npoints <- length(w)
for ( i in 1:npoints){
segments(w[i],Min[i],w[i],Max[i],lwd=1.5,col="blue")
}
legend("topleft",c("Disease progress curves","Weather-Severity"),
title="Description",lty=1,pch=c(3,19),col=c("black","blue"))

Line x Tester Analysis

Description

It makes the Line x Tester Genetic Analysis. It also estimates the general and specific combinatory ability effects and the line and tester genetic contribution.

Usage

lineXtester(replications, lines, testers, y)

Arguments

Details

ANOVA with parents and crosses
ANOVA for line X tester analysis
ANOVA for line X tester analysis including parents
GCA Effects: Lines Effects, Testers Effects and SCA Effects.
Standard Errors for Combining Ability Effects.
Genetic Components.
...
Proportional contribution of lines, testers and their interactions to total variance

Value

return anova(formula = Y ~ Replications + Treatments).
where the Treatments contains parents, crosses and crosses vs Parents.
The crosses contains Lines, Testers and its interaction .

Author(s)

Felipe de Mendiburu

References

Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979. Hierarchial and factorial mating designs for quantitative genetic analysis in tetrasomic potato. R. Ortis; A.Golmirzaie. Theor Appl Genet (2002) 104:675-679

See Also

AMMI

Examples

# see structure line by testers
library(agricolae)
# example 1
data(heterosis)
site1<-subset(heterosis,heterosis[,1]==1)
output1<-with(site1,lineXtester(Replication, Female, Male, v2))
# example 2
data(LxT)
str(LxT)
output2<-with(LxT,lineXtester(replication, line, tester, yield))

Data of molecular markers

Description

A partial study on 27 molecular markers.

Usage

data(markers)

Format

A data frame with 23 observations on the following 27 variables.

marker1

a numeric vector

marker2

a numeric vector

marker3

a numeric vector

marker4

a numeric vector

marker5

a numeric vector

marker6

a numeric vector

marker7

a numeric vector

marker8

a numeric vector

marker9

a numeric vector

marker10

a numeric vector

marker11

a numeric vector

marker12

a numeric vector

marker13

a numeric vector

marker14

a numeric vector

marker15

a numeric vector

marker16

a numeric vector

marker17

a numeric vector

marker18

a numeric vector

marker19

a numeric vector

marker20

a numeric vector

marker21

a numeric vector

marker22

a numeric vector

marker23

a numeric vector

marker24

a numeric vector

marker25

a numeric vector

marker26

a numeric vector

marker27

a numeric vector

Source

International Potato Center Lima-Peru.

References

International Potato Center Lima-Peru.

Examples

library(agricolae)
data(markers)
str(markers)

Data of yield of melon in a Latin square experiment

Description

An irrigation system evaluation by exudation using four varieties of melon, under modality of sowing, SIMPLE ROW. The goal is to analyze the behavior of three hybrid melon varieties and one standard.

Usage

data(melon)

Format

A data frame with 16 observations on the following 4 variables.

row

a numeric vector

col

a numeric vector

variety

a factor with levels V1 V2 V3 V4

yield

a numeric vector

Details

Varieties: Hibrido Mission (V1); Hibrido Mark (V2); Hibrido Topfligth (V3); Hibrido Hales Best Jumbo (V4).

Source

Tesis. "Evaluacion del sistema de riego por exudacion utilizando cuatro variedades de melon, bajo modalidad de siembra, SIMPLE HILERA". Alberto Angeles L. Universidad Agraria la Molina - Lima Peru.

References

Universidad Nacional Agraria la molina.

Examples

library(agricolae)
data(melon)
str(melon)

Random generation by Montecarlo

Description

Random generation form data, use function density and parameters

Usage

montecarlo(data, k, ...)

Arguments

Value

Generate random numbers with empirical distribution.

Author(s)

Felipe de Mendiburu

See Also

density

Examples

library(agricolae)
r<-rnorm(50, 10,2)
montecarlo(r, k=100, kernel="epanechnikov")
# other example
h<-hist(r,plot=FALSE)
montecarlo(h, k=100)
# other example
breaks<-c(0, 150, 200, 250, 300)
counts<-c(10, 20, 40, 30)
op<-par(mfrow=c(1,2),cex=0.8,mar=c(2,3,0,0))
h1<-graph.freq(x=breaks,counts=counts,plot=FALSE)
r<-montecarlo(h, k=1000)
plot(h1,frequency = 3,ylim=c(0,0.008))
text(90,0.006,"Population\n100 obs.")
h2<-graph.freq(r,breaks,frequency = 3,ylim=c(0,0.008))
lines(density(r),col="blue")
text(90,0.006,"Montecarlo\n1000 obs.")
par(op)

Data of native potato

Description

An evaluation of the number, weight and size of 24 native potatoes varieties.

Usage

data(natives)

Format

A data frame with 876 observations on the following 4 variables.

variety

a numeric vector

number

a numeric vector

weight

a numeric vector

size

a numeric vector

Source

International Potato Center. CIP - Lima Peru.

Examples

library(agricolae)
data(natives)
str(natives)

Nonadditivity model test

Description

The resistance for the transformable nonadditivity, due to J. W. Tukey, is based on the detection of a curvilinear relation between y-est(y) and est(y). A freedom degree for the transformable nonadditivity.

Usage

nonadditivity(y, factor1, factor2, df, MSerror)

Arguments

Details

Only two factor: Block and treatment or factor 1 and factor 2.

Value

P, Q and non-additivity analysis of variance

Author(s)

Felipe de Mendiburu

References

1. Steel, R.; Torri,J; Dickey, D.(1997) Principles and Procedures of Statistics A Biometrical Approach

2. George E.P. Box; J. Stuart Hunter and William G. Hunter. Statistics for experimenters. Wile Series in probability and statistics

Examples

library(agricolae)
data(potato )
potato[,1]<-as.factor(potato[,1])
model<-lm(cutting ~ date + variety,potato)
df<-df.residual(model)
MSerror<-deviance(model)/df
analysis<-with(potato,nonadditivity(cutting, date, variety, df, MSerror))

Normal curve on the histogram

Description

A normal distribution graph elaborated from the histogram previously constructed. The average and variance are obtained from the data grouped in the histogram.

Usage

normal.freq(histogram, frequency=1, ...)

Arguments

Author(s)

Felipe de Mendiburu

See Also

polygon.freq, table.freq, stat.freq, inter.freq, sturges.freq, join.freq, ogive.freq, graph.freq

Examples

library(agricolae)
data(growth)
#startgraph
h1<-with(growth,hist(height,col="green",xlim=c(6,14)))
normal.freq(h1,col="blue")
#endgraph
#startgraph
h2<-with(growth,graph.freq(height,col="yellow",xlim=c(6,14),frequency=2))
normal.freq(h2,frequency=2)
#endgraph

Plotting the ogive from a histogram

Description

It plots the cumulative relative .frequencies with the intervals of classes defined in the histogram.

Usage

ogive.freq(histogram,type="",xlab="",ylab="",axes="",las=1,...)

Arguments

Value

Ogive points.

Author(s)

Felipe de Mendiburu

See Also

polygon.freq, table.freq, stat.freq, inter.freq, sturges.freq, join.freq, graph.freq, normal.freq

Examples

library(agricolae)
data(growth)
h<-graph.freq(growth$height,plot=FALSE)
points<-ogive.freq(h,col="red",frame=FALSE,
xlab="Height", ylab="Accumulated relative frequency", main="ogive")
plot(points,type="b",pch=16,las=1,bty="l")

Ordering the treatments according to the multiple comparison

Description

This function allows us to compare the treatments averages or the adding of their ranges with the minimal significant difference which can vary from one comparison to another one.

Usage

order.group(trt, means, N, MSerror, Tprob, std.err, parameter=1, snk=0, 
DFerror=NULL,alpha=NULL,sdtdif=NULL,vartau=NULL,console)

Arguments

Details

This function was changed by orderPvalue function that use agricolae. Now the grouping in agricolae is with the probability of the treatments differences and alpha level.

Value

The output is data frame.

Note

It is considered 81 labels as maximum for the formation of groups, greater number will not have label.

Author(s)

Felipe de Mendiburu

See Also

orderPvalue

Examples

library(agricolae)
treatments <- c("A","B","C","D","E","F")
means<-c(20,40,35,72,49,58)
std.err<-c(1.2, 2, 1.5, 2.4, 1, 3.1)
replications <- c(4,4,3,4,3,3)
MSerror <- 55.8
value.t <- 2.1314
groups<-order.group(treatments,means,replications,MSerror,value.t,std.err,console=FALSE)
print(groups)

Grouping the treatments averages in a comparison with a minimum value

Description

When there are treatments and their respective values, these can be compared with a minimal difference of meaning.

Usage

orderPvalue(treatment, means, alpha, pvalue, console)

Arguments

Value

The means and groups for treatments.

Note

It is considered 81 labels as maximum for the formation of groups, greater number will not have label.

Author(s)

Felipe de Mendiburu

Examples

library(agricolae)
treatments <- c("A","B","C")
means<-c(2,5,3)
alpha <- 0.05
pvalue<-matrix(1,nrow=3,ncol=3)
pvalue[1,2]<-pvalue[2,1]<-0.03
pvalue[1,3]<-pvalue[3,1]<-0.10
pvalue[2,3]<-pvalue[3,2]<-0.06
out<-orderPvalue(treatments,means,alpha,pvalue,console=TRUE)
barplot(out[,1],names.arg = row.names(out),col=colors()[84:87])
legend("topright",as.character(out$groups),pch=15,col=colors()[84:87],box.col=0)

Data Potato Wild

Description

Potato Wild

Usage

data(pamCIP)

Format

A data frame with 43 observations on the following 107 variables. Rownames: code and genotype's name. column data: molecular markers.

Details

To study the molecular markers in Wild.

Source

Laboratory data.

References

International Potato Center Lima-Peru (CIP)

Examples

library(agricolae)
data(pamCIP)
str(pamCIP)

Data of Paracsho biodiversity

Description

A locality in Peru. A biodiversity.

Usage

data(paracsho)

Format

A data frame with 110 observations on the following 6 variables.

date

a factor with levels 15-12-05 17-11-05 18-10-05 20-09-05 22-06-05 23-08-05 28-07-05

plot

a factor with levels PARACSHO

Treatment

a factor with levels CON SIN

Orden

a factor with levels COLEOPTERA DIPTERA HEMIPTERA HYMENOPTERA LEPIDOPTERA NEUROPTERA NEUROPTERO NOCTUIDAE

Family

a factor with levels AGROMYZIDAE ANTHOCORIDAE ANTHOMYIIDAE ANTHOMYLIDAE BLEPHAROCERIDAE BRACONIDAE BROCONIDAE CALUPHORIDAE CECIDOMYIDAE CHENEUMONIDAE CHNEUMONIDAE CHRYOMELIDAE CICADELLIDAE CULICIDAE ERIOCPAMIDAE HEMEROBIIDAE ICHNEUMONIDAE LOUCHAPIDAE MIRIDAE MUSCIDAE MUSICADAE MUSLIDAE MYCETOPHILIDAE MYCETOPHILIIDAE NENPHALIDAE NOCLUIDAE NOCTERIDAE NOCTUIDAE PERALIDAE PIPUNCULIDAE PROCTOTRUPIDAE PSYLLIDAE PYRALIDAE SARCOPHAGIDAE SARCOPILAGIDAE SCATOPHAGIDAE SCATOPHOGIDAE SCIARIDAE SERSIDAE SYRPHIDAE TACHINIDAE TIPULIDAE

Number.of.specimens

a numeric vector

Details

Country Peru, Deparment Junin, province Tarma, locality Huasahuasi.

Source

Entomology dataset.

References

International Potato Center.

Examples

library(agricolae)
data(paracsho)
str(paracsho)

Path Analysis

Description

If the cause and effect relationship is well defined, it is possible to represent the whole system of variables in a diagram form known as path-analysis. The function calculates the direct and indirect effects and uses the variables correlation or covariance.

Usage

path.analysis(corr.x, corr.y)

Arguments

Details

It is necessary first to calculate the correlations.

Value

Direct and indirect effects and residual Effect^2.

Author(s)

Felipe de Mendiburu

References

Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979

See Also

correlation

Examples

# Path analysis. Multivarial Analysis. Anderson. Prentice Hall, pag 616
library(agricolae)
# Example 1
corr.x<- matrix(c(1,0.5,0.5,1),c(2,2))
corr.y<- rbind(0.6,0.7)
names<-c("X1","X2")
dimnames(corr.x)<-list(names,names)
dimnames(corr.y)<-list(names,"Y")
path.analysis(corr.x,corr.y)
# Example 2
# data of the progress of the disease related bacterial wilt to the ground
# for the component CE Ca K2 Cu
data(wilt)
data(soil)
x<-soil[,c(3,12,14,20)]
y<-wilt[,14]
cor.y<-correlation(y,x)$correlation
cor.x<-correlation(x)$correlation
path.analysis(cor.x,cor.y)

PLOT AMMI

Description

Biplot AMMI.

Usage

## S3 method for class 'AMMI'
plot(x,first=1,second=2,third=3,number=FALSE,gcol=NULL,ecol=NULL,
angle=25,lwd=1.8,length=0.1,xlab=NULL,ylab=NULL,xlim=NULL,ylim=NULL,...)

Arguments

Details

Produce graphs biplot.

Author(s)

Felipe de Mendiburu

See Also

AMMI

Examples

library(agricolae)
data(plrv)
model<- with(plrv,AMMI(Locality, Genotype, Rep, Yield))
# biplot PC2 vs PC1
plot(model)
## plot PC1 vs Yield
plot(model,0,1,gcol="blue",ecol="green")

Histogram

Description

In many situations it has intervals of class defined with its respective frequencies. By means of this function, the graphic of frequency is obtained and it is possible to superpose the normal distribution, polygon of frequency, Ojiva and to construct the table of complete frequency.

Usage

## S3 method for class 'graph.freq'
plot(x, breaks=NULL,counts=NULL,frequency=1,plot=TRUE,
nclass=NULL,xlab="",ylab="",axes = "",las=1,...)

Arguments

Value

Author(s)

Felipe de Mendiburu

See Also

polygon.freq, table.freq, stat.freq,inter.freq,sturges.freq, join.freq,ogive.freq, normal.freq

Examples

library(agricolae)
data(genxenv)
yield <- subset(genxenv$YLD,genxenv$ENV==2)
yield <- round(yield,1)
h<- graph.freq(yield,axes=FALSE, frequency=1, ylab="frequency",col="yellow")
axis(1,h$breaks)
axis(2,seq(0,20,0.1))
# To reproduce histogram.
h1 <- plot(h, col="blue", frequency=2,border="red", density=8,axes=FALSE, 
xlab="YIELD",ylab="relative")
axis(1,h$breaks)
axis(2,seq(0,.4,0.1))
# summary, only frecuency
limits <-seq(10,40,5)
frequencies <-c(2,6,8,7,3,4)
#startgraph
h<-graph.freq(limits,counts=frequencies,col="bisque",xlab="Classes")
polygon.freq(h,col="red")
title( main="Histogram and polygon of frequency",
ylab=".frequency")
#endgraph
# Statistics
measures<-stat.freq(h)
print(measures)
# frequency table full
round(table.freq(h),2)
#startgraph
# ogive
ogive.freq(h,col="red",type="b",ylab="Accumulated relative frequency",
xlab="Variable")
# only frequency polygon
h<-graph.freq(limits,counts=frequencies,border=FALSE,col=NULL,xlab="  ",ylab="")
title( main="Polygon of frequency",
xlab="Variable", ylab="Frecuency")
polygon.freq(h,col="blue")
grid(col="brown")
#endgraph
# Draw curve for Histogram
h<- graph.freq(yield,axes=FALSE, frequency=3, ylab="f(yield)",col="yellow")
axis(1,h$breaks)
axis(2,seq(0,0.18,0.03),las=2)
lines(density(yield), col = "red", lwd = 2)
title("Draw curve for Histogram")

Plotting the multiple comparison of means

Description

It plots bars of the averages of treatments to compare. It uses the objects generated by a procedure of comparison like LSD, HSD, Kruskall, Waller-Duncan, Friedman or Durbin. It can also display the 'average' value over each bar in a bar chart.

Usage

## S3 method for class 'group'
plot(x,variation=c("range","IQR","SE","SD"), decreasing = TRUE,
              horiz=FALSE,col=NULL,xlim=NULL,ylim=NULL,main=NULL,cex=NULL,hy=0,...)

Arguments

Details

The output is a vector that indicates the position of the treatments on the coordinate axes.

Author(s)

Felipe de Mendiburu

See Also

BIB.test, DAU.test, duncan.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waerden.test, waller.test

Examples

library(agricolae)
data(sweetpotato)
model<-aov(yield~virus,data=sweetpotato)
comparison<- LSD.test(model,"virus",alpha=0.01,group=TRUE)
#startgraph
op<-par(cex=1.5)
plot(comparison,horiz=TRUE,xlim=c(0,50),las=1)
title(cex.main=0.8,main="Comparison between\ntreatment means",xlab="Yield",ylab="Virus")
#endgraph
par(op)

Data for an analysis in split-plot

Description

Experimental data in blocks, factor A in plots and factor B in sub-plots.

Usage

data(plots)

Format

A data frame with 18 observations on the following 5 variables.

block

a numeric vector

plot

a factor with levels p1 p2 p3 p4 p5 p6

A

a factor with levels a1 a2

B

a factor with levels b1 b2 b3

yield

a numeric vector

Source

International Potato Center. CIP

Examples

library(agricolae)
data(plots)
str(plots)
plots[,1] <-as.factor(plots[,1])
# split-plot analysis
model <- aov(yield ~ block + A + Error(plot)+ B + A:B, data=plots)
summary(model)
b<-nlevels(plots$B)
a<-nlevels(plots$A)
r<-nlevels(plots$block)
dfa <- df.residual(model$plot)
Ea <-deviance(model$plot)/dfa
dfb <- df.residual(model$Within)
Eb <-deviance(model$Within)/dfb
Eab <- (Ea +(b-1)*Eb)/(b*r)
# Satterthwaite
dfab<-(Ea +(b-1)*Eb)^2/(Ea^2/dfa +((b-1)*Eb)^2/dfb)
# Comparison A, A(b1), A(b2), A(b3)
comparison1 <-with(plots,LSD.test(yield,A,dfa,Ea))
comparison2 <-with(plots,LSD.test(yield[B=="b1"],A[B=="b1"],dfab,Eab))
comparison3 <-with(plots,LSD.test(yield[B=="b2"],A[B=="b2"],dfab,Eab))
comparison4 <-with(plots,LSD.test(yield[B=="b3"],A[B=="b3"],dfab,Eab))
# Comparison B, B(a1), B(a2)
comparison5 <-with(plots,LSD.test(yield,B,dfb,Eb))
comparison6 <-with(plots,LSD.test(yield[A=="a1"],B[A=="a1"],dfb,Eb))
comparison7 <-with(plots,LSD.test(yield[A=="a2"],B[A=="a2"],dfb,Eb))

Data clones from the PLRV population

Description

Six environments: Ayacucho, La Molina 02, San Ramon 02, Huancayo, La Molina 03, San Ramon 03.

Usage

data(plrv)

Format

A data frame with 504 observations on the following 6 variables.

Genotype

a factor with levels 102.18 104.22 121.31 141.28 157.26 163.9 221.19 233.11 235.6 241.2 255.7 314.12 317.6 319.20 320.16 342.15 346.2 351.26 364.21 402.7 405.2 406.12 427.7 450.3 506.2 Canchan Desiree Unica

Locality

a factor with levels Ayac Hyo-02 LM-02 LM-03 SR-02 SR-03

Rep

a numeric vector

WeightPlant

a numeric vector

WeightPlot

a numeric vector

Yield

a numeric vector

Source

International Potato Center Lima-Peru

References

International Potato Center Lima-Peru

Examples

library(agricolae)
data(plrv)
str(plrv)

The polygon of frequency on the histogram

Description

The polygon is constructed single or on a histogram. It is necessary to execute the function previously hist.

Usage

polygon.freq(histogram, frequency=1, ...)

Arguments

Author(s)

Felipe de Mendiburu Delgado

See Also

polygon.freq, table.freq, stat.freq, inter.freq, sturges.freq, join.freq, graph.freq, normal.freq

Examples

library(agricolae)
data(growth)
#startgraph
h1<-with(growth,hist(height,border=FALSE,xlim=c(6,14)))
polygon.freq(h1,frequency=1,col="red")
#endgraph
#startgraph
h2<-with(growth,graph.freq(height,frequency=2,col="yellow",xlim=c(6,14)))
polygon.freq(h2,frequency=2,col="red")
#endgraph

Data of cutting

Description

A study on the yield of two potato varieties performed at the CIP experimental station.

Usage

data(potato)

Format

A data frame with 18 observations on the following 4 variables.

date

a numeric vector

variety

a factor with levels Canchan Unica

harvest

a numeric vector

cutting

a numeric vector

Source

Experimental data.

References

International Potato Center

Examples

library(agricolae)
data(potato)
str(potato)

Data of assessment of the population in the soil R.solanacearum

Description

The assessment of the population of R.solanacearum on the floor took place after 48 hours of infestation, during days 15, 29, 43, 58, and 133 days after the infestation soil. More information on soil data(soil).

Usage

data(ralstonia)

Format

A data frame with 13 observations on the following 8 variables.

place

a factor with levels Chmar Chz Cnt1 Cnt2 Cnt3 Hco1 Hco2 Hco3 Hyo1 Hyo2 Namora SR1 SR2

Day2

a numeric vector

Day15

a numeric vector

Day29

a numeric vector

Day43

a numeric vector

Day58

a numeric vector

Day73

a numeric vector

Day133

a numeric vector

Details

Logarithm average counts of colonies on plates containing half of M-SMSA 3 repetitions (3 plates by repetition) incubated at 30 degrees centigrade for 48 hours. log(1+UFC/g soil)

Source

Experimental field, 2004. Data Kindly provided by Dr. Sylvie Priou, Liliam Gutarra and Pedro Aley.

References

International Potato Center. CIP - Lima Peru.

Examples

library(agricolae)
data(ralstonia)
str(ralstonia)

Homologation of regressions

Description

It makes the regressions homogeneity test for a group of treatments where each observation presents a linearly dependent reply from another one. There is a linear function in every treatment. The objective is to find out if the linear models of each treatment come from the same population.

Usage

reg.homog(trt, x, y)

Arguments

Value

list objects:
Number regressions.
Residual.
Difference of regression.
DF.homgeneity (homogenity degree free).
DF.Residual (degree free error).
F.value. Test statitics.
P.value. P Value (Significant
Criterion. conclusion

Author(s)

Felipe de Mendiburu

References

Book in Spanish: Metodos estadisticos para la investigacion. Calzada Benza 1960

Examples

library(agricolae)
data(frijol)
evaluation<-with(frijol,reg.homog(technology,index,production))
# Example 2. Applied Regression Analysis a Research tools
# 1988. John O.Rawlings. Wadsworth & brooks/cole Advanced Books
# & Software. Pacific Grove. Califonia.
# Statistics/probability. Series
LineNumber<-c(rep("39","30"),rep("52","30"))
PlantingDate<-rep(c("16","20","21"),20)
HeadWt <- c(2.5,3.0,2.2,2.2,2.8,1.8,3.1,2.8,1.6,4.3,2.7,2.1,2.5,2.6,3.3,4.3,
2.8,3.8,3.8,2.6,3.2,4.3,2.6,3.6,1.7,2.6,4.2,3.1,3.5,1.6,2.0,4.0,1.5,2.4,2.8,
1.4,1.9,3.1,1.7,2.8,4.2,1.3,1.7,3.7,1.7,3.2,3.0,1.6,2.0,2.2,1.4,2.2,2.3,1.0,
2.2,3.8,1.5,2.2,2.0,1.6)
Ascorbic <-c(51,65,54,55,52,59,45,41,66,42,51,54,53,41,45,50,45,49,50,51,49,
52,45,55,56,61,49,49,42,68,58,52,78,55,70,75,67,57,70,61,58,84,67,47,71,68,
56,72,58,72,62,63,63,68,56,54,66,72,60,72)
trt<-paste(LineNumber,PlantingDate,sep="-")
output<-reg.homog(trt,HeadWt,Ascorbic)

Resampling to find the optimal number of markers

Description

This process finds the curve of CV for a different number of markers which allows us to determine the number of optimal markers for a given relative variability. A method of the curvature.

Usage

resampling.cv(A, size, npoints)

Arguments

Value

lm(formula = CV ~ I(1/marker))
Table with variation coefficient by number of markers

Author(s)

Felipe de Mendiburu

References

Efron, B., Tibshirani, R. (1993) An Introduction to the Boostrap. Chapman and Hall/CRC

See Also

cv.similarity, similarity

Examples

library(agricolae)
#example table of molecular markers
data(markers)
study<-resampling.cv(markers,size=1,npoints=15)
#
# Results of the model
summary(study$model)
coef<-coef(study$model)
py<-predict(study$model)
Rsq<-summary(study$model)$"r.squared"
table.cv <- data.frame(study$table.cv,estimate=py)
print(table.cv)

# Plot CV
#startgraph
limy<-max(table.cv[,2])+10
plot(table.cv[,c(1,2)],col="red",frame=FALSE,xlab="number of markers",
ylim=c(10,limy), ylab="CV",cex.main=0.8,main="Estimation of the number of markers")
ty<-quantile(table.cv[,2],1)
tx<-median(table.cv[,1])
tz<-quantile(table.cv[,2],0.95)
text(tx,ty, cex=0.8,as.expression(substitute(CV == a + frac(b,markers),
list(a=round(coef[1],2),b=round(coef[2],2)))) )
text(tx,tz,cex=0.8,as.expression(substitute(R^2==r,list(r=round(Rsq,3)))))

# Plot CV = a + b/n.markers
fy<-function(x,a,b) a+b/x
x<-seq(2,max(table.cv[,1]),length=50)
y <- coef[1] + coef[2]/x
lines(x,y,col="blue")
#grid(col="brown")
rug(table.cv[,1])
#endgraph

Resampling for linear models

Description

This process consists of finding the values of P-value by means of a re-sampling (permutation) process along with the values obtained by variance analysis.

Usage

resampling.model(model,data,k,console=FALSE)

Arguments

Value

Model solution with resampling.

Author(s)

Felipe de Mendiburu

References

Efron, B., Tibshirani, R. (1993) An Introduction to the Boostrap. Chapman and Hall/CRC Phillip I. Good, (2001) Resampling Methods. Birkhauser. Boston . Basel . Berlin

See Also

simulation.model

Examples

#example 1 Simple linear regression
library(agricolae)
data(clay)
model<-"ralstonia ~ days"
analysis<-resampling.model(model,clay,k=2,console=TRUE)

#example 2 Analysis of variance: RCD
data(sweetpotato)
model<-"yield~virus"
analysis<-resampling.model(model,sweetpotato,k=2,console=TRUE)

#example 3 Simple linear regression
data(Glycoalkaloids)
model<-"HPLC ~ spectrophotometer"
analysis<-resampling.model(model,Glycoalkaloids,k=2,console=TRUE)

#example 4 Factorial in RCD

data(potato)
potato[,1]<-as.factor(potato[,1])
potato[,2]<-as.factor(potato[,2])
model<-"cutting~variety + date + variety:date"
analysis<-resampling.model(model,potato,k=2,console=TRUE)

Data of Grain yield of rice variety IR8

Description

The data correspond to the yield of rice variety IR8 (g/m2) for land uniformity studies. The growing area is 18x36 meters.

Usage

data(rice)

Format

A data frame with 36 observations on the following 18 variables.

V1

a numeric vector

V2

a numeric vector

V3

a numeric vector

V4

a numeric vector

V5

a numeric vector

V6

a numeric vector

V7

a numeric vector

V8

a numeric vector

V9

a numeric vector

V10

a numeric vector

V11

a numeric vector

V12

a numeric vector

V13

a numeric vector

V14

a numeric vector

V15

a numeric vector

V16

a numeric vector

V17

a numeric vector

V18

a numeric vector

Details

Table 12.1 Measuring Soil Heterogeneity

Source

Statistical Procedures for Agriculture Research. Second Edition. Kwanchai A. Gomez and Arturo A. Gomez. 1976. USA Pag. 481.

References

Statistical Procedures for Agriculture Research. Second Edition. Kwanchai A. Gomez and Arturo A. Gomez. 1976. USA

Examples

library(agricolae)
data(rice)
str(rice)

Multiple comparisons, scheffe

Description

Scheffe 1959, method is very general in that all possible contrasts can be tested for significance and confidence intervals can be constructed for the corresponding linear. The test is conservative.

Usage

scheffe.test(y, trt, DFerror, MSerror, Fc, alpha = 0.05, group=TRUE, main = NULL,
console=FALSE )

Arguments

Details

It is necessary first makes a analysis of variance.

if y = model, then to apply the instruction:
scheffe.test (model, "trt", alpha = 0.05, group = TRUE, main = NULL, console = FALSE)
where the model class is aov or lm.

Value

Author(s)

Felipe de Mendiburu

References

Robert O. Kuehl. 2nd ed. Design of experiments. Duxbury, copyright 2000.
Steel, R.; Torri,J; Dickey, D.(1997) Principles and Procedures of Statistics A Biometrical Approach. pp189

See Also

BIB.test, DAU.test, duncan.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, Median.test, PBIB.test, REGW.test, SNK.test, waerden.test, waller.test, plot.group

Examples

library(agricolae)
data(sweetpotato)
model<-aov(yield~virus, data=sweetpotato)
comparison <- scheffe.test(model,"virus", group=TRUE,console=TRUE,
main="Yield of sweetpotato\nDealt with different virus")
# Old version scheffe.test()
df<-df.residual(model)
MSerror<-deviance(model)/df
Fc<-anova(model)["virus",4]
out <- with(sweetpotato,scheffe.test(yield, virus, df, MSerror, Fc))
print(out)

Matrix of similarity in binary data

Description

It finds the similarity matrix of binary tables (1 and 0).

Usage

similarity(A)

Arguments

Value

Distance matrix. Class = dist.

Author(s)

Felipe de Mendiburu

See Also

cv.similarity, resampling.cv

Examples

#example table of molecular markers
library(agricolae)
data(markers)
distance<-similarity(markers)
#startgraph
tree<-hclust(distance,method="mcquitty")
plot(tree,col="blue")
#endgraph

Simulation of the linear model under normality

Description

This process consists of validating the variance analysis results using a simulation process of the experiment. The validation consists of comparing the calculated values of each source of variation of the simulated data with respect to the calculated values of the original data. If in more than 50 percent of the cases they are higher than the real one, then it is considered favorable and the probability reported by the ANOVA is accepted, since the P-Value is the probability of (F > F.value).

Usage

simulation.model(model,file, categorical = NULL,k,console=FALSE)

Arguments

Value

Author(s)

Felipe de Mendiburu

See Also

resampling.model

Examples

library(agricolae)
#example 1
data(clay)
model<-"ralstonia ~ days"
simulation.model(model,clay,k=15,console=TRUE)
#example 2
data(sweetpotato)
model<-"yield~virus"
simulation.model(model,sweetpotato,categorical=1,k=15,console=TRUE)
#example 3
data(Glycoalkaloids)
model<-"HPLC ~ spectrophotometer"
simulation.model(model,Glycoalkaloids,k=15,console=TRUE)
#example 4
data(potato)
model<-"cutting~date+variety"
simulation.model(model,potato,categorical=c(1,2,3),k=15,console=TRUE)

Data for AMMI without repetition

Description

Data frame for AMMI analysis with 50 genotypes in 5 environments.

Usage

data(sinRepAmmi)

Format

A data frame with 250 observations on the following 3 variables.

ENV

a factor with levels A1 A2 A3 A4 A5

GEN

a numeric vector

YLD

a numeric vector

Source

Experimental data.

References

International Potato Center - Lima Peru.

Examples

library(agricolae)
data(sinRepAmmi)
str(sinRepAmmi)

Finding the skewness coefficient

Description

It returns the skewness of a distribution. It is similar to SAS.

Usage

skewness(x)

Arguments

Value

The skewness of x.

See Also

kurtosis

Examples

library(agricolae)
x<-c(3,4,5,2,3,4,NA,5,6,4,7)
skewness(x)
# value is 0,3595431, is slightly asimetrica (positive) to the right

Data of soil analysis for 13 localities

Description

We analyzed the physical and chemical properties of different soils, as full characterization of soil and special analysis of micro-elements. These analyses were conducted in the laboratory analysis of soils, plants, water and fertilizers in the La Molina National Agrarian University (UNALM). To which the different soil samples were dried to the environment, screened (mesh 0.5xo, 5 mm) and sterilized by steam 4 to 5 hours with a Lindinger Steam aerator SA150 and SA700, with the possible aim of eliminating bacteria saprophytic or antagonists to prevent the growth of bacteria (R.solanacearum).

Usage

data(soil)

Format

A data frame with 13 observations on the following 23 variables.

place

a factor with levels Chmar Chz Cnt1 Cnt2 Cnt3 Hco1 Hco2 Hco3 Hyo1 Hyo2 Namora SR1 SR2

pH

a numeric vector

EC

a numeric vector, electrical conductivity

CaCO3

a numeric vector

MO

a numeric vector

CIC

a numeric vector

P

a numeric vector

K

a numeric vector

sand

a numeric vector

slime

a numeric vector

clay

a numeric vector

Ca

a numeric vector

Mg

a numeric vector

K2

a numeric vector

Na

a numeric vector

Al_H

a numeric vector

K_Mg

a numeric vector

Ca_Mg

a numeric vector

B

a numeric vector

Cu

a numeric vector

Fe

a numeric vector

Mn

a numeric vector

Zn

a numeric vector

Details

Cnt1= Canete, Cnt2=Valle Dulce(Canete), Cnt3=Valle Grande(Canete), Chz=Obraje-Carhuaz(Ancash), Chmar=Chucmar-Chota(Huanuco, Hco1= Mayobamba-Chinchao(Huanuco), Hco2=Nueva Independencia-Chinchao(Huanuco), Hco3=San Marcos-Umari(Huanuco), Hyo1=La Victoria-Huancayo(Junin), Hyo1=El Tambo-Huancayo(Junin), Namora=Namora(Cajamarca), SR1= El Milagro-San Ramon(Junin), Sr2=La Chinchana-San Ramon(Junin).

Source

Experimental field, 2004. Data Kindly provided by Dr. Sylvie Priou, Liliam Gutarra and Pedro Aley.

References

International Potato Center - Lima, PERU.

Examples

library(agricolae)
data(soil)
str(soil)

Splip-Plot analysis

Description

The variance analysis of a split plot design is divided into two parts: the plot-factor analysis and the sub-plot factor analysis.

Usage

sp.plot(block, pplot, splot, Y)

Arguments

Details

The split-plot design is specifically suited for a two-factor experiment on of the factors is assigned to main plot (main-plot factor), the second factor, called the subplot factor, is assigned into subplots. The model is mixed, the blocks are random and the study factors are fixed applied according to the design.

Value

ANOVA: Splip plot analysis

Author(s)

Felipe de Mendiburu

References

Statistical procedures for agricultural research. Kwanchai A. Gomez, Arturo A. Gomez. Second Edition. 1984.

See Also

ssp.plot, strip.plot, design.split, design.strip

Examples

library(agricolae)
data(plots)
model<-with(plots,sp.plot(block,A,B,yield))
# with aov
plots[,1]<-as.factor(plots[,1])
AOV <- aov(yield ~ block + A*B + Error(block/A),data=plots)
summary(AOV)

Split-split-Plot analysis

Description

The variance analysis of a split-split plot design is divided into three parts: the main-plot, subplot and sub-subplot analysis.

Usage

ssp.plot(block, pplot, splot, ssplot, Y)

Arguments

Details

The split-split-plot design is an extension of the split-plot design to accommodate a third factor: one factor in main-plot, other in subplot and the third factor in sub-subplot. The model is mixed, the blocks are random and the study factors are fixed applied according to the design.

Value

ANOVA: Splip Split plot analysis

Author(s)

Felipe de Mendiburu

References

Statistical procedures for agricultural research. Kwanchai A. Gomez, Arturo A. Gomez. Second Edition. 1984.

See Also

sp.plot, strip.plot, design.split, design.strip

Examples

# Statistical procedures for agricultural research, pag 143
# Grain Yields of Three Rice Varieties Grown under 
#Three Management practices and Five Nitrogen levels; in a
#split-split-plot design with nitrogen as main-plot, 
#management practice as subplot, and variety as sub-subplot 
#factores, with three replications.
library(agricolae)
f <- system.file("external/ssp.csv", package="agricolae")
ssp<-read.csv(f)
model<-with(ssp,ssp.plot(block,nitrogen,management,variety,yield))
gla<-model$gl.a; glb<-model$gl.b; glc<-model$gl.c
Ea<-model$Ea; Eb<-model$Eb; Ec<-model$Ec
op<-par(mfrow=c(1,3),cex=0.6)
out1<-with(ssp,LSD.test(yield,nitrogen,gla,Ea,console=TRUE))
out2<-with(ssp,LSD.test(yield,management,glb,Eb,console=TRUE))
out3<-with(ssp,LSD.test(yield,variety,glc,Ec,console=TRUE))
plot(out1,xlab="Nitrogen",las=1,variation="IQR")
plot(out2,xlab="Management",variation="IQR")
plot(out3,xlab="Variety",variation="IQR")
# with aov
ssp$block<-factor(ssp$block)
ssp$nitrogen<-factor(ssp$nitrogen)
ssp$management<-factor(ssp$management)
ssp$variety<-factor(ssp$variety)
AOV<-aov(yield ~  block + nitrogen*management*variety + Error(block/nitrogen/management),data=ssp)
summary(AOV)
par(op)

Nonparametric stability analysis

Description

A method based on the statistical ranges of the study variable per environment for the stability analysis.

Usage

stability.nonpar(data, variable = NULL, ranking = FALSE, console=FALSE)

Arguments

Value

Author(s)

Felipe de Mendiburu

References

Haynes K G, Lambert D H, Christ B J, Weingartner D P, Douches D S, Backlund J E, Fry W and Stevenson W. 1998. Phenotypic stability of resistance to late blight in potato clones evaluated at eight sites in the United States American Journal Potato Research 75, pag 211-217.

See Also

stability.par

Examples

library(agricolae)
data(haynes)
stability.nonpar(haynes,"AUDPC",ranking=TRUE,console=TRUE)
# Example 2
data(CIC)
data1<-CIC$comas[,c(1,6,7,17,18)]
data2<-CIC$oxapampa[,c(1,6,7,19,20)]
cic <- rbind(data1,data2)

means <- by(cic[,5], cic[,c(2,1)], function(x) mean(x,na.rm=TRUE))
means <-as.data.frame(means[,])
cic.mean<-data.frame(genotype=row.names(means),means)
cic.mean<-delete.na(cic.mean,"greater")
out<-stability.nonpar(cic.mean)
out$ranking
out$statistics

Stability analysis. SHUKLA'S STABILITY VARIANCE AND KANG'S

Description

This procedure calculates the stability variations as well as the statistics of selection for the yield and the stability. The averages of the genotype through the different environment repetitions are required for the calculations. The mean square error must be calculated from the joint variance analysis.

Usage

stability.par(data,rep,MSerror,alpha=0.1,main=NULL,cova = FALSE,name.cov=NULL,
file.cov=0,console=FALSE)

Arguments

Details

Stable (i) determines the contribution of each genotype to GE interaction by calculating var(i); (ii) assigns ranks to genotypes from highest to lowest yield receiving the rank of 1; (iii) calculates protected LSD for mean yield comparisons; (iv) adjusts yield rank according to LSD (the adjusted rank labeled Y); (v) determines significance of var(i) usign an aproximate F-test; (vi) assigns stability rating (S) as follows: -8, -4 and -2 for var(i) significant at the 0.01, 0.05 and 0.10 probability levels, and 0 for nonsignificant var(i) ( the higher the var(i), the less stable the genotype); (vii) sums adjusted yield rank, Y, and stability rating, S, for each genotype to determine YS(i) statistic; and (viii) calculates mean YS(i) and identifies genotypes (selection) with YS(i) > mean YS(i).

Value

Author(s)

Felipe de Mendiburu

References

Kang, M. S. 1993. Simultaneous selection for yield and stability: Consequences for growers. Agron. J. 85:754-757. Manjit S. Kang and Robert Mangari. 1995. Stable: A basic program for calculating stability and yield-stability statistics. Agron. J. 87:276-277

See Also

stability.nonpar

Examples

library(agricolae)
# example 1
# Experimental data,
# replication rep= 4
# Mean square error, MSerror = 1.8
# 12 environment
# 17 genotype  = 1,2,3,.., 17
# yield averages of 13 genotypes in localities
f <- system.file("external/dataStb.csv", package="agricolae")
dataStb<-read.csv(f)
stability.par(dataStb, rep=4, MSerror=1.8, alpha=0.1, main="Genotype",console=TRUE)

#example 2 covariable. precipitation
precipitation<- c(1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000,2100)
stability.par(dataStb, rep=4, MSerror=1.8, alpha=0.1, main="Genotype",
 cova=TRUE, name.cov="Precipitation", file.cov=precipitation,console=TRUE)

Descriptive measures of grouped data

Description

By this process the variance and central measures ar found: average, medium and mode of grouped data.

Usage

stat.freq(histogram)

Arguments

Value

Statistics of grouped data.

Author(s)

Felipe de mendiburu

See Also

polygon.freq, table.freq, graph.freq, inter.freq, sturges.freq, join.freq, ogive.freq, normal.freq

Examples

library(agricolae)
data(growth)
grouped<-with(growth,hist(height,plot=FALSE))
measures<-stat.freq(grouped)
print(measures)

Strip-Plot analysis

Description

The variance analysis of a strip-plot design is divided into three parts: the horizontal-factor analysis, the vertical-factor analysis, and the interaction analysis.

Usage

strip.plot(BLOCK, COL, ROW, Y)

Arguments

Details

The strip-plot design is specifically suited for a two-factor experiment in which the desired precision for measuring the interaction effects between the two factors is higher than that for measuring the main efect two factors

Value

Data and analysis of the variance of the strip plot design.

Author(s)

Felipe de Mendiburu

References

Statistical procedures for agricultural research. Kwanchai A. Gomez, Arturo A. Gomez. Second Edition. 1984.

See Also

ssp.plot, sp.plot, design.split, design.strip

Examples

# Yield
library(agricolae)
data(huasahuasi)
YIELD<-huasahuasi$YIELD
market <- YIELD$y1da + YIELD$y2da
non_market <- YIELD$y3da
yield <- market + non_market
model<-with(YIELD,strip.plot(block, clon, trt, yield))
out1<-with(YIELD,LSD.test(yield,clon,model$gl.a,model$Ea))
oldpar<-par(mar=c(3,8,1,1),cex=0.8)
plot(out1,xlim=c(0,80),horiz=TRUE,las=1)
out2<-with(YIELD,LSD.test(yield,trt,model$gl.b,model$Eb))
plot(out2,xlim=c(0,80),horiz=TRUE,las=1)
par(oldpar)

Class intervals for a histogram, the rule of Sturges

Description

if k=0 then classes: k = 1 + log(n,2). if k > 0, fixed nclass.

Usage

sturges.freq(x,k=0)

Arguments

Value

Statistics of sturges for a histogram.

Author(s)

Felipe de mendiburu

References

Reza A. Hoshmand. 1988. Statistical Methods for Agricultural Sciences, Timber Press, Incorporated, pag 18-21.

See Also

polygon.freq, table.freq, stat.freq, inter.freq, graph.freq, join.freq, ogive.freq, normal.freq

Examples

library(agricolae)
data(natives)
classes<-with(natives,sturges.freq(size))
# information of the classes
breaks <- classes$breaks
breaks
#startgraph
# Histogram with the established classes
h<-with(natives,graph.freq(size,breaks,frequency=1, col="yellow",axes=FALSE,
    xlim=c(0,0.12),main="",xlab="",ylab=""))
axis(1,breaks,las=2)
axis(2,seq(0,400,50),las=2)
title(main="Histogram of frequency\nSize of the tubercule of the Oca",
xlab="Size of the oca", ylab="Frequency")
#endgraph

frequency Table of a Histogram

Description

It finds the absolute, relative and accumulated frequencies with the class intervals defined from a previously calculated histogram by the "hist" of R function.

Usage

## S3 method for class 'graph.freq'
summary(object,...)

Arguments

Value

Frequency table.

Author(s)

Felipe de Mendiburu

See Also

polygon.freq, stat.freq, graph.freq, inter.freq, sturges.freq, join.freq, ogive.freq, normal.freq

Examples

library(agricolae)
data(growth)
h2<-with(growth,graph.freq(height,plot=FALSE))
print(summary(h2),row.names=FALSE)

Data of sweetpotato yield

Description

The data correspond to an experiment with costanero sweetpotato made at the locality of the Tacna department, southern Peru. The effect of two viruses (Spfmv and Spcsv) was studied. The treatments were the following: CC (Spcsv) = Sweetpotato chlorotic dwarf, FF (Spfmv) = Feathery mottle, FC (Spfmv y Spcsv) = Viral complex and OO (witness) healthy plants. In each plot, 50 sweetpotato plants were sown and 12 plots were employed. Each treatment was made with 3 repetitions and at the end of the experiment the total weight in kilograms was evaluated. The virus transmission was made in the cuttings and these were sown in the field.

Usage

data(sweetpotato)

Format

A data frame with 12 observations on the following 2 variables.

virus

a factor with levels cc fc ff oo

yield

a numeric vector

Source

Experimental field.

References

International Potato Center. CIP - Lima Peru

Examples

library(agricolae)
data(sweetpotato)
str(sweetpotato)

frequency Table of a Histogram

Description

It finds the absolute, relative and accumulated frequencies with the class intervals defined from a previously calculated histogram by the "hist" of R function.

Usage

table.freq(object)

Arguments

Value

Frequency table.

Author(s)

Felipe de Mendiburu

See Also

polygon.freq, stat.freq, graph.freq, inter.freq, sturges.freq, join.freq, ogive.freq, normal.freq

Examples

library(agricolae)
data(growth)
h2<-with(growth,graph.freq(height,plot=FALSE))
print(table.freq(h2),row.names=FALSE)

Statistics of data grouped by factors

Description

This process lies in finding statistics which consist of more than one variable, grouped or crossed by factors. The table must be organized by columns between variables and factors.

Usage

tapply.stat(y, x, stat = "mean")

Arguments

Value

Statistics of quantitative variables by categorical variables.

Author(s)

Felipe de Mendiburu

Examples

library(agricolae)
# case of 1 single factor
data(sweetpotato)
tapply.stat(sweetpotato[,2],sweetpotato[,1],mean)
with(sweetpotato,tapply.stat(yield,virus,sd))
with(sweetpotato,tapply.stat(yield,virus,function(x) max(x)-min(x)))
with(sweetpotato,tapply.stat(yield,virus,
function(x) quantile(x,0.75,6)-quantile(x,0.25,6)))
# other case
data(cotton)
with(cotton,tapply.stat(yield,cotton[,c(1,3,4)],mean))
with(cotton,tapply.stat(yield,cotton[,c(1,4)],max))
# Height of pijuayo
data(growth)
with(growth,tapply.stat(height, growth[,2:1], function(x) mean(x,na.rm=TRUE)))

Variance K, ties, Kendall

Description

The Kendall method in order to find the K variance.

Usage

vark(x, y)

Arguments

Details

Script in C to R.

Value

variance of K for Kendall's tau

Author(s)

Felipe de Mendiburu

References

Numerical Recipes in C. Second Edition.

See Also

cor.matrix, cor.vector, cor.mv

Examples

library(agricolae)
x <-c(1,1,1,4,2,2,3,1,3,2,1,1,2,3,2,1,1,2,1,2)
y <-c(1,1,2,3,4,4,2,1,2,3,1,1,3,4,2,1,1,3,1,2)
vark(x,y)

Multiple comparisons. The van der Waerden (Normal Scores)

Description

A nonparametric test for several independent samples.

Usage

waerden.test(y, trt, alpha=0.05, group=TRUE, main=NULL,console=FALSE)

Arguments

Details

The data consist of k samples of possibly unequal sample size.
The post hoc test is using the criterium Fisher's least
significant difference (LSD).

Value

Author(s)

Felipe de Mendiburu

References

Practical Nonparametrics Statistics. W.J. Conover, 1999

See Also

BIB.test, DAU.test, duncan.test, durbin.test, friedman, HSD.test, kruskal, LSD.test, Median.test, PBIB.test, REGW.test, scheffe.test, SNK.test, waller.test, plot.group

Examples

library(agricolae)
# example 1
data(corn)
out1<-with(corn,waerden.test(observation,method,group=TRUE))
print(out1$groups)
plot(out1)
out2<-with(corn,waerden.test(observation,method,group=FALSE))
print(out2$comparison)
# example 2
data(sweetpotato)
out<-with(sweetpotato,waerden.test(yield,virus,alpha=0.01,group=TRUE))
print(out)

Computations of Bayesian t-values for multiple comparisons

Description

A Bayes rule for the symmetric multiple comparisons problem.

Usage

waller(K, q, f, Fc)

Arguments

Details

K-RATIO (K): value specifies the Type 1/Type 2 error seriousness ratio for the Waller-Duncan test. Reasonable values for KRATIO are 50, 100, and 500, which roughly correspond for the two-level case to ALPHA levels of 0.1, 0.05, and 0.01. By default, the procedure uses the default value of 100.

Value

Waller value for the Waller and Duncan test.

Author(s)

Felipe de Mendiburu

References

Waller, R. A. and Duncan, D. B. (1969). A Bayes Rule for the Symmetric Multiple Comparison Problem, Journal of the American Statistical Association 64, pages 1484-1504.

Waller, R. A. and Kemp, K. E. (1976) Computations of Bayesian t-Values for Multiple Comparisons, Journal of Statistical Computation and Simulation, 75, pages 169-172.

Principles and procedures of statistics a biometrical approach Steel & Torry & Dickey. Third Edition 1997.

See Also

waller.test

Examples

# Table Duncan-Waller K=100, F=1.2 pag 649 Steel & Torry
library(agricolae)
K<-100
Fc<-1.2
q<-c(8,10,12,14,16,20,40,100)
f<-c(seq(4,20,2),24,30,40,60,120)
n<-length(q)
m<-length(f)
W.D <-rep(0,n*m)
dim(W.D)<-c(n,m)
for (i in 1:n) {
for (j in 1:m) {
W.D[i,j]<-waller(K, q[i], f[j], Fc)
}}
W.D<-round(W.D,2)
dimnames(W.D)<-list(q,f)
print(W.D)

Multiple comparisons, Waller-Duncan

Description

The Waller-Duncan k-ratio t test is performed on all main effect means in the MEANS statement. See the K-RATIO option for information on controlling details of the test.

Usage

waller.test(y, trt, DFerror, MSerror, Fc, K = 100, group=TRUE, main = NULL, 
console=FALSE)

Arguments