knitr::opts_chunk$set(echo = TRUE)
To install this github package (in R):
#if devtools is not installed yet: # install.packages("devtools") library(devtools) install_github("livioivil/rospo")
library(rospo) data(pal.unipd.76) X <- as.data.frame(matrix(rpois(30,3),3,10)) webplot.multi(X,col=pal.unipd.76) # set colors data(pal.uno) palette(pal.uno)
par(mar=c(1,1,1,1)) Y=matrix(rnorm(30),10,3) rownames(Y)=paste("obs",1:nrow(Y)) sv=svd(Y) pc.biplot(sv) ########### sv=svd(scale(Y,center=TRUE,scale=FALSE)) pc.biplot(sv,obs.names = TRUE) pc.biplot(sv,obs.opt = list(col=rep(1:2,5)))
set.seed(1) n=100 X=matrix(rnorm(n*3),n,3) X[,2]=sign(X[,2]) y=rnorm(n,X[,1]+X[,1]*X[,2]) D=data.frame(X) D$y=y # Regression model mod=lm(y~X1*X2+X3,data=D) summary(mod) predict_funct=function(newdata) predict(mod,newdata=newdata) plot_effects_individual(D,"X1","y",predict_funct=predict_funct,col.by = D$X2) # in this case the same as: # plot_effects_individual(D,"X1","y",predict_funct=predict_funct,col.by = D$X2,center_effs = FALSE) # Regression tree model require(rpart) mod=rpart(y~X1+X2+X3,data=D,control = list(cp=.0001)) print(mod) printcp(mod) predict_funct=function(newdata) predict(mod,newdata=newdata) plot_effects_individual(D,"X1","y",predict_funct=predict_funct,col.by = D$X2) plot_effects_individual(D,"X1","y",predict_funct=predict_funct,col.by = D$X2,center_effs = FALSE)
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