In livioivil/rospo: Utilities from associazionerospo.org

knitr::opts_chunk$set(echo = TRUE)

Utilities from associazionerospo.org

Set up

To install this github package (in R):

#if devtools is not installed yet: 
# install.packages("devtools") 
library(devtools)
install_github("livioivil/rospo")

Some examples

webplot

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)

biplot

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)))

Plot partial effects in a model

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)