Type: Package
Title: MultiGroup Method and Simulation Data Analysis
Version: 0.4.0
Maintainer: Carolina Millap/'an <cayoya19@gmail.com>
Description: Two method new of multigroup and simulation of data. The first technique called multigroup PCA (mgPCA) this multivariate exploration approach that has the idea of considering the structure of groups and / or different types of variables. On the other hand, the second multivariate technique called Multigroup Dimensionality Reduction (MDR) it is another multivariate exploration method that is based on projections. In addition, a method called Single Dimension Exploration (SDE) was incorporated for to analyze the exploration of the data. It could help us in a better way to observe the behavior of the multigroup data with certain variables of interest.
License: GPL-3
Encoding: UTF-8
Imports: mvtnorm, rlist, expm, stats, ggplot2, gridExtra, cowplot, plsgenomics, gplots, ggrepel, qgraph, mgm,lemon
Suggests: knitr, rmarkdown
ByteCompile: yes
VignetteBuilder: knitr
RoxygenNote: 7.2.3
NeedsCompilation: no
Packaged: 2024-07-07 23:06:39 UTC; jmuno
Author: Carolina Millap/'an [aut, cre], Esteban Vegas ORCID iD [aut], Ferran Reverter ORCID iD [aut], Josep M Oller ORCID iD [aut], Joel Mu/~noz ORCID iD [aut]
Repository: CRAN
Date/Publication: 2024-07-07 23:20:03 UTC

biplot methods

Description

biplot methods

Usage

BIplot(
  variates,
  loadings,
  prop_expl_var,
  comp = c(1, 2),
  group = NULL,
  rownamevar = T,
  rownameload = T
)

Arguments

variates

is the size of groups

loadings

is a vector of classes

prop_expl_var

data set

comp

component numeric

group

is a vector of groups

rownamevar

is a logical vector where TRUE is the label of the observations, if is FALSE, is index.

rownameload

is a logical vector where TRUE is the label of the vectors of loadings, if is FALSE, is index.

Value

return an grafics .

Examples

library(datasets)
obj<-pca(datos=iris[,-5],grupos=iris[,5],Plot=FALSE,center=TRUE,scale=TRUE)
BIplot(variates=obj$variates,loadings=obj$loadings,
      prop_expl_var=obj$prop_expl_var,comp=c(1,2),
      group=factor(as.numeric(iris[,5])),rownamevar=FALSE,rownameload=TRUE)

Simulation function of quantitative multigroup data under a multivariate normal distribution

Description

Simulation function of quantitative multigroup data under a multivariate normal distribution

Usage

fun.sim(g, mean1, d, n.var, sds2, corr)

Arguments

g

An vector of the size of each group

mean1

An vector of the population means structure

d

distance d for the structure of population means

n.var

2x1 dimension vector whose first component is the number of random variables to simulate and the second component number of noise variables to simulate

sds2

An vector of the variances to simulate for each group noise variables

corr

An vector of the correlation to simulate for each group and noise variables

Value

return an grafics

Examples

fun.sim(g=c(20,20),mean1=2,d=0,sds2=c(1,1,1),corr=c(0.5,0.5,0),n.var=c(50,1))

Performs a Multigroup Dimensionality Reduction (MDR) analysis in the given multigroup data matrix. Show MDR graphical output.

Description

Performs a Multigroup Dimensionality Reduction (MDR) analysis in the given multigroup data matrix. Show MDR graphical output.

Usage

mdr(group, data.x, c, Plot = T)

Arguments

group

is a vector of classes

data.x

quantitative data set

c

component numeric

Plot

grafics output of MDR

Value

return an grafics .

Examples

sim.list<-fun.sim(g=c(50,50,50),mean1=2,d=0,sds2=c(1,1,1,1),
corr=c(0.5,0.5,0.5,0),n.var=c(30,30))

mdr(group=as.factor(sim.list$grp),
data.x=sim.list$`lisx`,c=2)

Performs a Multigroup PCA analysis in the given multigroup data matrix. Show mgpca graphical output.

Description

Performs a Multigroup PCA analysis in the given multigroup data matrix. Show mgpca graphical output.

Usage

mgpca(
  mat.to.diag,
  mat.x,
  cls,
  Plot = TRUE,
  ncomp = 2,
  center = TRUE,
  scale = TRUE
)

Arguments

mat.to.diag

is a matrix with the data

mat.x

is a vector of classes

cls

group

Plot

grafics output of mgpca

ncomp

number of component

center

is a logical vector where TRUE is center (whether the variables should be shifted to be zero centered), if is FALSE, is original data.

scale

is a logical vector where TRUE is scale (indicating whether the variables should be scaled), if is FALSE, is original data.

Value

If simplify == TRUE class values. If simplify == FALSE, the result is a list of length nsim data.tables.

Examples

library(plsgenomics)
data(SRBCT)
mydata<-SRBCT$X
mydata<-mydata[1:50,1:5]
groups<-as.factor(SRBCT$Y)[1:50]
mat.to.diag1<-new.cov(x=mydata,cls=groups,A=diag(ncol(mydata)))
mgpca(mat.to.diag=mat.to.diag1,mat.x=as.matrix(mydata),
cls=groups,Plot=TRUE,ncomp=2,center = TRUE,scale = TRUE)

Function for the new covariance matrix in the multigroup PCA method

Description

Generates covariance matrix...

Usage

new.cov(x, cls, A)

Arguments

x

is a matrix with the data

cls

is a vector of classes

A

is a symmetric and positive definite matrix associated to inner product respect to the base of its vectorial space.

Value

return an grafics.

Examples

library(plsgenomics)
data(SRBCT)
mydata<-SRBCT$X
mydata<-mydata[1:50,1:20]
groups<-as.factor(SRBCT$Y)[1:50]
new.cov(x=mydata,cls=groups,A=diag(ncol(mydata)))

Performs a principal components analysis in the given data matrix. Show PCA graphical output.

Description

Performs a principal components analysis in the given data matrix. Show PCA graphical output.

Usage

pca(datos, grupos, Plot = TRUE, center = TRUE, scale = TRUE)

Arguments

datos

is a matrix with the data

grupos

is a vector of classes

Plot

vector logic for grafic

center

data set center by columns

scale

data set scaled by columns

Value

return an grafics.

Examples

library(plsgenomics)
data(SRBCT)
mydata<-SRBCT$X
mydata<-mydata[1:30,1:20]
groups<-as.factor(SRBCT$Y)[1:30]
pca(datos=mydata,grupos=groups,Plot=TRUE,center=TRUE,scale=TRUE)

Performs a Single Dimension Exploration (SDE) analysis in the given multigroup data matrix. Show SDE graphical output.

Description

Performs a Single Dimension Exploration (SDE) analysis in the given multigroup data matrix. Show SDE graphical output.

Usage

sde.method(mydata, groups, plt = FALSE)

Arguments

mydata

data set

groups

is a vector of classes

plt

grafics

Value

return an grafics .

Examples

sim.list2<-fun.sim(g=c(20,50,10),mean1=0.5,d=0,sds2=c(1,1,1,1),corr=c(0.1,0.5,0.5,0),
n.var=c(20,20))
datos2 <- as.data.frame(sim.list2$x)
datos2<-subset(datos2,select=-grp)
grupos <- sim.list2$grp
grupos<-factor(grupos,labels=c(1,2,3))
sde.method(mydata=datos2,groups=grupos,plt=FALSE)