Dear R-Experts,
Here below my reproducible example working but not entirely (working). What I
understand is that there is a problem of libraries library(hbrfit) and ... ?
How can I make it work entirely, many thanks for your precious help.
########SIMULATION STUDY 3 variables with 10% outliers n=2000
install.packages( "robustbase" )
install.packages( "MASS" )
install.packages( "quantreg" )
install.packages( "RobPer" )
install.packages("devtools") library("devtools")
install_github("kloke/hbrfit")
install.packages('http://www.stat.wmich.edu/mckean/Stat666/Pkgs/npsmReg2_0.1.1.tar.gz')
install.packages( "RobStatTM" )
library(robustbase)
library(MASS)
library(quantreg)
library(RobPer)
library(hbrfit)
library(RobStatTM)
n<-2000
x<-runif(n, 0, 5)
z <- rnorm(n, 2, 3)
a <- runif(n, 0, 5)
y_model<- 0.1*x - 0.5 * z - a + 10
y_obs <- y_model +c( rnorm(n*0.9, 0, 0.1), rnorm(n*0.1, 0, 0.5) )
fastMM <- lmrob( y_obs ~ x+z+a)
Huber <- rlm( y_obs ~ x+z+a)
Tukey <- rlm( y_obs ~ x+z+a, psi = psi.bisquare )
L1 <- rq( y_obs ~ x+z+a, tau = 0.5 )
fastTau <- FastTau(model.matrix(~newdata$x+newdata$z+newdata$a),newdata$y_obs)
HBR<-hbrfit(y_obs ~ x+z+a)
DCML <-lmrobdetDCML(y_obs ~ x+z+a)
MSE_fastMM<-mean((fastMM$fitted.values - y_model)^2)
MSE_Huber<-mean((Huber$fitted.values - y_model)^2)
MSE_Tukey<-mean((Tukey$fitted.values - y_model)^2)
MSE_L1<-mean((L1$fitted.values - y_model)^2)
MSE_fastTau<-mean((fastTau$fitted.values - y_model)^2)
MSE_HBR<-mean((HBR$fitted.values - y_model)^2)
MSE_DCML<-mean((DCML$fitted.values - y_model)^2)
MSE_fastMM
MSE_Huber
MSE_Tukey
MSE_L1
MSE_fastTau
MSE_HBR
MSE_DCML
###############
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