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knitr::opts_chunk$set( fig.width=6, fig.height=5, collapse = TRUE, comment = "#" )
The package contains functions to simulate multigroup data from a multivariate normal with different mean, matrix structures of variances and covariances, it will also consider not only biological signal but also considers noise variables for the different sizes of groups that can be balanced or unbalanced. It also contains functions with the new proposed multigroup methods.
For an overview of supported, methods, and defaults together with some basic examples see
You can install the development version from GitHub with
# install.packages("remotes") remotes::install_github("carolina/MultiGroupO")
library(MultiGroupO) sim.list<-fun.sim(g=c(30,30,30),mean1=2,d=0,sds2=c(1,1,1,1),corr=c(0.5,0.5,0.5,0), n.var=c(20,50)) datos1 <- as.data.frame(sim.list$x) datos1<-subset(datos1,select=-grp) grupos <- sim.list$grp pca(datos1,grupos,Plot=TRUE,center=TRUE,scale=FALSE) mat.to.diag1<-new.cov(datos1,cls=grupos,A=diag(ncol(datos1))) mgpca(mat.to.diag=mat.to.diag1,mat.x=as.matrix(datos1),cls=grupos,Plot=T,ncomp=2,center = TRUE,scale = TRUE)
library(MultiGroupO) sim.list<-fun.sim(g=c(30,30,30),mean1=2,d=0,sds2=c(1,1,1,1),corr=c(0.5,0.5,0.5,0), n.var=c(20,50)) datos1 <- as.data.frame(sim.list$x) datos1<-subset(datos1,select=-grp) grupos <- sim.list$grp mdr(group=grupos,data.x=sim.list$`lisx`,c=2)
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