Type: | Package |
Title: | Experimental Design and Analysis for Tree Improvement |
Version: | 1.1.0 |
Maintainer: | Muhammad Yaseen <myaseen208@gmail.com> |
Description: | Provides data sets and R Codes for E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing. |
Depends: | R (≥ 4.1.0) |
Imports: | car, dae, dplyr, emmeans, ggplot2, lmerTest, magrittr, predictmeans, stats, supernova |
License: | GPL-3 |
URL: | https://github.com/MYaseen208/eda4treeR https://CRAN.R-project.org/package=eda4treeR https://myaseen208.com/eda4treeR/ https://myaseen208.com/EDATR/ |
BugReports: | https://github.com/myaseen208/eda4treeR/issues |
LazyData: | TRUE |
RoxygenNote: | 7.3.2 |
Encoding: | UTF-8 |
Suggests: | testthat |
Note: | 1. Asian Development Bank (ADB), Islamabad, Pakistan. 2. Benazir Income Support Programme (BISP), Islamabad, Pakistan. 3. Department of Mathematics and Statistics, University of Agriculture Faisalabad, Pakistan. |
NeedsCompilation: | no |
Packaged: | 2024-09-13 21:21:45 UTC; myaseen208 |
Author: | Muhammad Yaseen |
Repository: | CRAN |
Date/Publication: | 2024-09-13 21:50:02 UTC |
Data for Example 2.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam2.1 is used to compare two seed lots by using single factor ANOVA.
Usage
data(DataExam2.1)
Format
A data.frame
with 16 rows and 2 variables.
seedlot
Two Seedlots Seed Orchad (SO) and routin plantation (P)
dbh
Diameter at breast height
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
data(DataExam2.1)
Data for Example 2.2 from Experimental Design and Analysis for Tree Improvement
Description
Exam2.2 is used to compare two seed lots by using ANOVA under RCB Design.
Usage
data(DataExam2.2)
Format
A data.frame
with 16 rows and 2 variables.
repl
repl
block
block
Seedlot
Two Seedlots Seed Orchad (SO) and routin plantation (P)
dbh
Diameter at breast height
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
data(DataExam2.2)
Data for Example 3.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam3.1 is part of data from Australian Centre for Agricultural Research (ACIAR) in Queensland, Australia (Experiment 309).
Usage
data(DataExam3.1)
Format
A data.frame
with 80 rows and 6 variables.
repl
Replication number of different Seedlots
PlotNo
Plot No of differnt Trees
seedlot
Seed Lot number
TreeNo
Tree number of Seedlots
ht
Height in meter
dgl
Diameter at ground level
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
data(DataExam3.1)
Data for Example 3.1.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam3.1.1 is part of data from Australian Centre for Agricultural Research (ACIAR) in Queensland, Australia (Experiment 309).
Usage
data(DataExam3.1.1)
Format
A data.frame
with 10 rows and 6 variables.
repl
Replication number of different Seedlots
PlotNo
Plot No of differnt Trees
seedlot
Seed Lot number
TreeNo
Tree number of Seedlots
ht
Height in meter
Var
Var
TreeCount
TreeCount
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
data(DataExam3.1.1)
Data for Example 4.3 from Experimental Design and Analysis for Tree Improvement
Description
Exam4.3 presents the germination count data for 4 Pre-Treatments and 6 Seedlots.
Usage
data(DataExam4.3)
Format
A data.frame
with 72 rows and 8 variables.
rep
Replication number of Treatment
row
Row number of different Seedlots
column
Column number of differnt Trees
seedlot
Seed lot number
treat
Treatment types
count
Number of germinated seeds out of 25
percent
Germination Percentage
contcomp
Control or Trated Plot
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
data(DataExam4.3)
Data for Example 4.3.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam4.3.1 presents the germination count data for 4 Pre-Treatments and 6 Seedlots.
Usage
data(DataExam4.3.1)
Format
A data.frame
with 72 rows and 8 variables.
Row
Row number of different Seedlots
Column
Column number of differnt Trees
Replication
Replication number of Treatment
Contcomp
Control or Trated Plot
Pretreatment
Treatment types
SeedLot
Seed lot number
GerminationCount
Number of germinated seeds out of 25
Percent
Germination Percentage
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
data(DataExam4.3.1)
Data for Example 4.4 from Experimental Design and Analysis for Tree Improvement
Description
Exam4.4 presents the height means for 4 seedlots under factorial arrangement for two levels of Fertilizer and two levels of Irrigation.
Usage
data(DataExam4.4)
Format
A data.frame
with 32 rows and 5 variables.
repl
Replication number
irrig
Irrigation type
fert
Fertilizer type
seedlot
Seed Lot number
height
Height of the plants
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
data(DataExam4.4)
Data for Example 5.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam5.1 presents the height of 27 seedlots from 4 sites.
Usage
data(DataExam5.1)
Format
A data.frame
with 108 rows and 4 variables.
site
Sites for the experiment
seedlot
Seed lot number
ht
Height of the plants
sitemean
Mean Height of Each Site
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
data(DataExam5.1)
Data for Example 5.2 from Experimental Design and Analysis for Tree Improvement
Description
Exam5.2 presents the height of 37 seedlots from 6 sites.
Usage
data(DataExam5.2)
Format
A data.frame
with 108 rows and 4 variables.
site
Sites for the experiment
seedlot
Seed lot number
ht
Height of the plants
sitemean
Mean Height of Each Site
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
data(DataExam5.2)
Data for Example 6.2 from Experimental Design and Analysis for Tree Improvement
Description
Exam 6.2 Dbh mean, Dbh varince and number of trees per plot from 3 provinces("PNG","Sabah","Queensland") with 4 replicationsof 48 families.
Usage
data(DataExam6.2)
Format
A data.frame
with 192 rows and 7 variables.
Replication
Replication number of different Families
Plot.number
Plot number of differnt Trees
Family
Family Numuber
Province
Province of family
Dbh.mean
Average Diameter at breast height of trees within plot
Dbh.variance
Variance of Diameter at breast height of trees within plot
Dbh.count
Number of trees within plot
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
Examples
data(DataExam6.2)
Data for Example 8.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam8.1 presents the Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries.
Usage
data(DataExam8.1)
Format
A data.frame
with 236 rows and 8 variables.
repl
There are 4 replication for the design
row
Experiment is conducted under 6 rows
\
col
Experiment is conducted under 4 columns
inoc
Seedling were inoculated for 2 different time periods half for one week and half for seven weeks
prov
provenance
Country
Data for different seedlots was collected from 18 countries
Dbh
Diameter at breast height
Country.1
Recoded Country lables
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
data(DataExam8.1)
Data for Example 8.2 from Experimental Design and Analysis for Tree Improvement
Description
Exam8.2 presents the Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries.
Usage
data(DataExam8.2)
Format
A data.frame
with 236 rows and 8 variables.
repl
There are 4 replication for the design
row
Experiment is conducted under 6 rows
\
column
Experiment is conducted under 4 columns
clonenum
Clonenum
contcompf
Contcompf
standard
Standard
clone
Clone
dbh
dbhmean
dbhvar
dbhvariance
ht
htmean
htvar
htvariance
count
count
contcompv
Contcompv
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
data(DataExam8.2)
Example 2.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam2.1 is used to compare two seed lots by using single factor ANOVA.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam2.1)
# Pg. 22
fmtab2.3 <- lm(formula = dbh ~ seedlot, data = DataExam2.1)
# Pg. 23
anova(fmtab2.3)
# Pg. 23
emmeans(object = fmtab2.3, specs = ~ seedlot)
emmip(object = fmtab2.3, formula = ~ seedlot) +
theme_classic()
Example 2.2 from Experimental Design and Analysis for Tree Improvement
Description
Exam2.2 is used to compare two seed lots by using ANOVA under RCB Design.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam2.2)
# Pg. 24
fmtab2.5 <-
lm(
formula = dbh ~ block + seedlot
, data = DataExam2.2
)
# Pg. 26
anova(fmtab2.5)
# Pg. 26
emmeans(object = fmtab2.5, specs = ~ seedlot)
emmip(object = fmtab2.5, formula = ~ seedlot) +
theme_classic()
Data for Example 3.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam3.1 is part of data from Australian Centre for Agricultural Research (ACIAR) in Queensland, Australia (Experiment 309).
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
library(supernova)
data(DataExam3.1)
# Pg. 28
fmtab3.3 <-
lm(
formula = ht ~ repl*seedlot
, data = DataExam3.1
)
fmtab3.3ANOVA1 <-
anova(fmtab3.3) %>%
mutate(
"F value" =
c(
anova(fmtab3.3)[1:2, 3]/anova(fmtab3.3)[3, 3]
, anova(fmtab3.3)[3, 4]
, NA
)
)
# Pg. 33 (Table 3.3)
fmtab3.3ANOVA1 %>%
mutate(
"Pr(>F)" =
c(
NA
, pf(
q = fmtab3.3ANOVA1[2, 4]
, df1 = fmtab3.3ANOVA1[2, 1]
, df2 = fmtab3.3ANOVA1[3, 1], lower.tail = FALSE
)
, NA
, NA
)
)
# Pg. 33 (Table 3.3)
emmeans(object = fmtab3.3, specs = ~ seedlot)
# Pg. 34 (Figure 3.2)
ggplot(
mapping = aes(
x = fitted.values(fmtab3.3)
, y = residuals(fmtab3.3)
)
) +
geom_point(size = 2) +
labs(
x = "Fitted Values"
, y = "Residual"
) +
theme_classic()
# Pg. 33 (Table 3.4)
DataExam3.1m <- DataExam3.1
DataExam3.1m[c(28, 51, 76), 5] <- NA
DataExam3.1m[c(28, 51, 76), 6] <- NA
fmtab3.4 <-
lm(
formula = ht ~ repl*seedlot
, data = DataExam3.1m
)
fmtab3.4ANOVA1 <-
anova(fmtab3.4) %>%
mutate(
"F value" =
c(
anova(fmtab3.4)[1:2, 3]/anova(fmtab3.4)[3, 3]
, anova(fmtab3.4)[3, 4]
, NA
)
)
# Pg. 33 (Table 3.4)
fmtab3.4ANOVA1 %>%
mutate(
"Pr(>F)" =
c(
NA
, pf(
q = fmtab3.4ANOVA1[2, 4]
, df1 = fmtab3.4ANOVA1[2, 1]
, df2 = fmtab3.4ANOVA1[3, 1], lower.tail = FALSE
)
, NA
, NA
)
)
# Pg. 33 (Table 3.4)
emmeans(object = fmtab3.4, specs = ~ seedlot)
Example 3.1.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam3.1.1 is part of data from Australian Centre for Agricultural Research (ACIAR) in Queensland, Australia (Experiment 309).
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam3.1.1)
# Pg. 36
fm3.8 <-
lm(
formula = ht ~ repl + seedlot
, data = DataExam3.1.1
)
# Pg. 40
anova(fm3.8)
# Pg. 40
emmeans(object = fm3.8, specs = ~seedlot)
emmip(object = fm3.8, formula = ~seedlot) +
theme_classic()
Example 4.3 from Experimental Design and Analysis for Tree Improvement
Description
Exam4.3 presents the germination count data for 4 Pre-Treatments and 6 Seedlots.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam4.3)
# Pg. 50
fm4.2 <-
aov(
formula =
percent ~ repl + contcomp + seedlot +
treat/contcomp + contcomp/seedlot +
treat/contcomp/seedlot
, data = DataExam4.3
)
# Pg. 54
anova(fm4.2)
# Pg. 54
model.tables(x = fm4.2, type = "means")
emmeans(object = fm4.2, specs = ~ contcomp)
emmeans(object = fm4.2, specs = ~ seedlot)
emmeans(object = fm4.2, specs = ~ contcomp + treat)
emmeans(object = fm4.2, specs = ~ contcomp + seedlot)
emmeans(object = fm4.2, specs = ~ contcomp + treat + seedlot)
DataExam4.3 %>%
dplyr::group_by(treat, contcomp, seedlot) %>%
dplyr::summarize(Mean = mean(percent))
RESFIT <-
data.frame(
residualvalue = residuals(fm4.2)
, fittedvalue = fitted.values(fm4.2)
)
ggplot(mapping = aes(
x = fitted.values(fm4.2)
, y = residuals(fm4.2))) +
geom_point(size = 2) +
labs(
x = "Fitted Values"
, y = "Residuals"
) +
theme_classic()
Example 4.3.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam4.3.1 presents the germination count data for 4 Pre-Treatments and 6 Seedlots.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam4.3)
# Pg. 57
fm4.4 <-
aov(
formula = percent ~ repl + treat*seedlot
, data = DataExam4.3 %>%
filter(treat != "control")
)
# Pg. 57
anova(fm4.4)
model.tables(x = fm4.4, type = "means", se = TRUE)
emmeans(object = fm4.4, specs = ~ treat)
emmeans(object = fm4.4, specs = ~ seedlot)
emmeans(object = fm4.4, specs = ~ treat * seedlot)
Example 4.4 from Experimental Design and Analysis for Tree Improvement
Description
Exam4.4 presents the height means for 4 seedlots under factorial arrangement for two levels of Fertilizer and two levels of Irrigation.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam4.4)
# Pg. 58
fm4.6 <-
aov(
formula = height ~ repl + irrig*fert*seedlot +
Error(repl/irrig:fert)
, data = DataExam4.4
)
# Pg. 61
summary(fm4.6)
# Pg. 61
model.tables(x = fm4.6, type = "means")
# Pg. 61
emmeans(object = fm4.6, specs = ~ irrig)
emmip(object = fm4.6, formula = ~ irrig) +
theme_classic()
Example 5.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam5.1 presents the height of 27 seedlots from 4 sites.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam5.1)
# Pg.68
fm5.4 <-
lm(
formula = ht ~ site*seedlot
, data = DataExam5.1
)
# Pg. 73
anova(fm5.4)
# Pg. 73
emmeans(object = fm5.4, specs = ~ site)
emmeans(object = fm5.4, specs = ~ seedlot)
ANOVAfm5.4 <- anova(fm5.4)
ANOVAfm5.4[4, 1:3] <- c(208, 208*1040, 1040)
ANOVAfm5.4[3, 4] <- ANOVAfm5.4[3, 3]/ANOVAfm5.4[4, 3]
ANOVAfm5.4[3, 5] <-
pf(
q = ANOVAfm5.4[3, 4]
, df1 = ANOVAfm5.4[3, 1]
, df2 = ANOVAfm5.4[4, 1]
, lower.tail = FALSE
)
# Pg. 73
ANOVAfm5.4
# Pg. 80
DataExam5.1 %>%
filter(seedlot %in% c("13653", "13871")) %>%
ggplot(
data = .
, mapping = aes(
x = sitemean
, y = ht
, color = seedlot
, shape = seedlot
)
) +
geom_point() +
geom_smooth(
method = lm
, se = FALSE
, fullrange = TRUE
) +
theme_classic() +
labs(
x = "SiteMean"
, y = "SeedLot Mean"
)
Tab5.10 <-
DataExam5.1 %>%
summarise(Mean = mean(ht), .by = seedlot) %>%
left_join(
DataExam5.1 %>%
nest_by(seedlot) %>%
mutate(fm1 = list(lm(ht ~ sitemean, data = data))) %>%
summarise(Slope = coef(fm1)[2])
, by = "seedlot"
)
# Pg. 81
Tab5.10
ggplot(data = Tab5.10, mapping = aes(x = Mean, y = Slope)) +
geom_point(size = 2) +
theme_bw() +
labs(
x = "SeedLot Mean"
, y = "Regression Coefficient"
)
DevSS1 <-
DataExam5.1 %>%
nest_by(seedlot) %>%
mutate(fm1 = list(lm(ht ~ sitemean, data = data))) %>%
summarise(SSE = anova(fm1)[2, 2]) %>%
ungroup() %>%
summarise(Dev = sum(SSE)) %>%
as.numeric()
ANOVAfm5.4[2, 2]
length(levels(DataExam5.1$SeedLot))
ANOVAfm5.4.1 <-
rbind(
ANOVAfm5.4[1:3, ]
, c(
ANOVAfm5.4[2, 1]
, ANOVAfm5.4[3, 2] - DevSS1
, (ANOVAfm5.4[3, 2] - DevSS1)/ANOVAfm5.4[2, 1]
, NA
, NA
)
, c(
ANOVAfm5.4[3, 1]-ANOVAfm5.4[2, 1]
, DevSS1
, DevSS1/(ANOVAfm5.4[3, 1]-ANOVAfm5.4[2, 1])
, DevSS1/(ANOVAfm5.4[3, 1]-ANOVAfm5.4[2, 1])/ANOVAfm5.4[4, 3]
, pf(
q = DevSS1/(ANOVAfm5.4[3, 1]-ANOVAfm5.4[2, 1])/ANOVAfm5.4[4, 3]
, df1 = ANOVAfm5.4[3, 1]-ANOVAfm5.4[2, 1]
, df2 = ANOVAfm5.4[4, 1]
, lower.tail = FALSE
)
)
, ANOVAfm5.4[4, ]
)
rownames(ANOVAfm5.4.1) <-
c(
"Site"
, "seedlot"
, "site:seedlot"
, " regressions"
, " deviations"
, "Residuals"
)
# Pg. 82
ANOVAfm5.4.1
Example 5.2 from Experimental Design and Analysis for Tree Improvement
Description
Exam5.2 presents the height of 37 seedlots from 6 sites.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam5.2)
# Pg. 75
fm5.7 <-
lm(
formula = ht ~ site*seedlot
, data = DataExam5.2
)
# Pg. 77
anova(fm5.7)
fm5.9 <-
lm(
formula = ht ~ site*seedlot
, data = DataExam5.2
)
# Pg. 77
anova(fm5.9)
ANOVAfm5.9 <- anova(fm5.9)
ANOVAfm5.9[4, 1:3] <- c(384, 384*964, 964)
ANOVAfm5.9[3, 4] <- ANOVAfm5.9[3, 3]/ANOVAfm5.9[4, 3]
ANOVAfm5.9[3, 5] <-
pf(
q = ANOVAfm5.9[3, 4]
, df1 = ANOVAfm5.9[3, 1]
, df2 = ANOVAfm5.9[4, 1]
, lower.tail = FALSE
)
# Pg. 77
ANOVAfm5.9
Tab5.14 <-
DataExam5.2 %>%
summarise(
Mean = round(mean(ht, na.rm = TRUE), 0)
, .by = seedlot
) %>%
left_join(
DataExam5.2 %>%
nest_by(seedlot) %>%
mutate(fm2 = list(lm(ht ~ sitemean, data = data))) %>%
summarise(Slope = round(coef(fm2)[2], 2))
, by = "seedlot"
) %>%
as.data.frame()
# Pg. 81
Tab5.14
DevSS2 <-
DataExam5.2 %>%
nest_by(seedlot) %>%
mutate(fm2 = list(lm(ht ~ sitemean, data = data))) %>%
summarise(SSE = anova(fm2)[2, 2]) %>%
ungroup() %>%
summarise(Dev = sum(SSE)) %>%
as.numeric()
ANOVAfm5.9.1 <-
rbind(
ANOVAfm5.9[1:3, ]
, c(
ANOVAfm5.9[2, 1]
, ANOVAfm5.9[3, 2] - DevSS2
, (ANOVAfm5.9[3, 2] - DevSS2)/ANOVAfm5.9[2, 1]
, NA
, NA
)
, c(
ANOVAfm5.9[3, 1]-ANOVAfm5.9[2, 1]
, DevSS2
, DevSS2/(ANOVAfm5.9[3, 1]-ANOVAfm5.9[2, 1])
, DevSS2/(ANOVAfm5.9[3, 1]-ANOVAfm5.9[2, 1])/ANOVAfm5.9[4, 3]
, pf(
q = DevSS2/(ANOVAfm5.9[3, 1]-ANOVAfm5.9[2, 1])/ANOVAfm5.9[4, 3]
, df1 = ANOVAfm5.9[3, 1]-ANOVAfm5.9[2, 1]
, df2 = ANOVAfm5.9[4, 1]
, lower.tail = FALSE
)
)
, ANOVAfm5.9[4, ]
)
rownames(ANOVAfm5.9.1) <-
c(
"site"
, "seedlot"
, "site:seedlot"
, " regressions"
, " deviations"
, "Residuals"
)
# Pg. 82
ANOVAfm5.9.1
Code <-
c(
"a","a","a","a","b","b","b","b"
, "c","d","d","d","d","e","f","g"
, "h","h","i","i","j","k","l","m"
,"n","n","n","o","p","p","q","r"
, "s","t","t","u","v"
)
Tab5.14$Code <- Code
ggplot(
data = Tab5.14
, mapping = aes(x = Mean, y = Slope)
) +
geom_point(size = 2) +
geom_text(
mapping = aes(label = Code)
, hjust = -0.5
, vjust = -0.5
) +
theme_bw() +
labs(
x = "SeedLot Mean"
, y = "Regression Coefficient"
)
Example 6.2 from Experimental Design and Analysis for Tree Improvement
Description
Exam 6.2 Dbh mean, Dbh varince and number of trees per plot from 3 provinces("PNG","Sabah","Queensland") with 4 replications of 48 families.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam6.2)
DataExam6.2.1 <-
DataExam6.2 %>%
filter(Province == "PNG")
# Pg. 94
fm6.3 <-
lm(
formula = Dbh.mean ~ Replication + Family
, data = DataExam6.2.1
)
b <- anova(fm6.3)
HM <- function(x){length(x)/sum(1/x)}
w <- HM(DataExam6.2.1$Dbh.count)
S2 <- b[["Mean Sq"]][length(b[["Mean Sq"]])]
Sigma2t <- mean(DataExam6.2.1$Dbh.variance)
sigma2m <- S2-(Sigma2t/w)
fm6.3.1 <-
lmer(
formula = Dbh.mean ~ 1 + Replication + (1|Family)
, data = DataExam6.2.1
, REML = TRUE
)
# Pg. 104
# summary(fm6.3.1)
varcomp(fm6.3.1)
sigma2f <- 0.2584
h2 <- (sigma2f/(0.3))/(Sigma2t + sigma2m + sigma2f)
cbind(hmean = w, Sigma2t, sigma2m, sigma2f, h2)
fm6.4 <-
lm(
formula = Dbh.mean ~ Replication+Family
, data = DataExam6.2
)
b <- anova(fm6.4)
HM <- function(x){length(x)/sum(1/x)}
w <- HM(DataExam6.2$Dbh.count)
S2 <- b[["Mean Sq"]][length(b[["Mean Sq"]])]
Sigma2t <- mean(DataExam6.2$Dbh.variance)
sigma2m <- S2-(Sigma2t/w)
fm6.4.1 <-
lmer(
formula = Dbh.mean ~ 1 + Replication + Province + (1|Family)
, data = DataExam6.2
, REML = TRUE
)
# Pg. 107
varcomp(fm6.4.1)
sigma2f <- 0.3514
h2 <- (sigma2f/(0.3))/(Sigma2t+sigma2m+sigma2f)
cbind(hmean = w, Sigma2t, sigma2m, sigma2f, h2)
fm6.7.1 <-
lmer(
formula = Dbh.mean ~ 1+Replication+(1|Family)
, data = DataExam6.2.1
, REML = TRUE
)
# Pg. 116
varcomp(fm6.7.1)
sigma2f[1] <- 0.2584
fm6.7.2<-
lmer(
formula = Ht.mean ~ 1 + Replication + (1|Family)
, data = DataExam6.2.1
, REML = TRUE
)
# Pg. 116
varcomp(fm6.7.2)
sigma2f[2] <- 0.2711
fm6.7.3 <-
lmer(
formula = Sum.means ~ 1 + Replication + (1|Family)
, data = DataExam6.2.1
, REML = TRUE
, control = lmerControl()
)
# Pg. 116
varcomp(fm6.7.3)
sigma2f[3] <- 0.873
sigma2xy <- 0.5*(sigma2f[3]-sigma2f[1]-sigma2f[2])
GenCorr <- sigma2xy/sqrt(sigma2f[1]*sigma2f[2])
cbind(
S2x = sigma2f[1]
, S2y = sigma2f[2]
, S2.x.plus.y = sigma2f[3]
, GenCorr
)
Example 8.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam8.1 presents the Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam8.1)
# Pg. 141
fm8.4 <-
aov(
formula = dbh ~ inoc + Error(repl/inoc) +
inoc*country*prov
, data = DataExam8.1
)
# Pg. 150
summary(fm8.4)
# Pg. 150
model.tables(x = fm8.4, type = "means")
RESFit <-
data.frame(
fittedvalue = fitted.aovlist(fm8.4)
, residualvalue = proj(fm8.4)$Within[,"Residuals"]
)
ggplot(
data = RESFit
, mapping = aes(x = fittedvalue, y = residualvalue)
) +
geom_point(size = 2) +
labs(
x = "Residuals vs Fitted Values"
, y = ""
) +
theme_bw()
# Pg. 153
fm8.6 <-
aov(
formula = terms(
dbh ~ inoc + repl + col +
repl:row + repl:col +
prov + inoc:prov
, keep.order = TRUE
)
, data = DataExam8.1
)
summary(fm8.6)
Example 8.1.1 from Experimental Design and Analysis for Tree Improvement
Description
Exam8.1.1 presents the Mixed Effects Analysis of Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries given in Example 8.1.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam8.1)
# Pg. 155
fm8.8 <-
lmerTest::lmer(
formula = dbh ~ 1 + repl + col + prov +
(1|repl:row) + (1|repl:col)
, data = DataExam8.1
, REML = TRUE
)
# Pg. 157
## Not run:
varcomp(fm8.8)
## End(Not run)
anova(fm8.8)
anova(fm8.8, ddf = "Kenward-Roger")
predictmeans(model = fm8.8, modelterm = "repl")
predictmeans(model = fm8.8, modelterm = "col")
predictmeans(model = fm8.8, modelterm = "prov")
# Pg. 161
RCB1 <-
aov(dbh ~ prov + repl, data = DataExam8.1)
RCB <-
emmeans(RCB1, specs = "prov") %>%
as_tibble()
Mixed <-
emmeans(fm8.8, specs = "prov") %>%
as_tibble()
table8.9 <-
left_join(
x = RCB
, y = Mixed
, by = "prov"
, suffix = c(".RCBD", ".Mixed")
)
print(table8.9)
Example 8.1.2 from Experimental Design & Analysis for Tree Improvement
Description
Exam8.1.2 presents the Analysis of Nested Seedlot Structure of Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries given in Example 8.1.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam8.1)
# Pg. 167
fm8.11 <-
aov(
formula = dbh ~ country + country:prov
, data = DataExam8.1
)
b <- anova(fm8.11)
Res <- length(b[["Sum Sq"]])
df <- 119
MSS <- 0.1951
b[["Df"]][Res] <- df
b[["Sum Sq"]][Res] <- MSS*df
b[["Mean Sq"]][Res] <- b[["Sum Sq"]][Res]/b[["Df"]][Res]
b[["F value"]][1:Res-1] <-
b[["Mean Sq"]][1:Res-1]/b[["Mean Sq"]][Res]
b[["Pr(>F)"]][Res-1] <-
df(
b[["F value"]][Res-1]
, b[["Df"]][Res-1]
, b[["Df"]][Res]
)
b
emmeans(fm8.11, specs = "country")
Example 8.2 from Experimental Design and Analysis for Tree Improvement
Description
Exam8.2 presents the Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries.
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
References
E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).
See Also
Examples
library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
data(DataExam8.2)
# Pg.
fm8.2 <-
lmerTest::lmer(
formula = dbh ~ repl + column +
contcompf + contcompf:standard +
(1|repl:row) + (1|repl:column) +
(1|contcompv:clone)
, data = DataExam8.2
)
## Not run:
varcomp(fm8.2)
## End(Not run)
anova(fm8.2)
Anova(fm8.2, type = "II", test.statistic = "Chisq")
predictmeans(model = fm8.2, modelterm = "repl")
predictmeans(model = fm8.2, modelterm = "column")
emmeans(object = fm8.2, specs = ~contcompf|standard)