Simulations study not working entirely...
Hi, After I install all dependencies your example seems fine ```
MSE_fastMM
[1] 2.629064e-05
MSE_Huber
[1] 1.826184e-05
MSE_Tukey
[1] 2.622499e-05
MSE_L1
[1] 1.044155e-05
MSE_fastTau
[1] NaN
MSE_HBR
[1] 1.60821e-05
MSE_DCML
[1] 9.519007e-06
sessionInfo()
R version 3.6.0 (2019-04-26) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows >= 8 x64 (build 9200) Matrix products: default locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C [5] LC_TIME=English_United States.1252 attached base packages: [1] splines stats graphics grDevices utils datasets methods base other attached packages: [1] hbrfit_0.02 Rfit_0.23.0 RobStatTM_1.0.1 fit.models_0.5-14 [5] RobPer_1.2.2 rgenoud_5.8-3.0 BB_2019.10-1 quantreg_5.51 [9] SparseM_1.77 MASS_7.3-51.4 robustbase_0.93-5 ``` There is no error or warning, except that MSE_fastTau is an NaN. What problem are you looking for? Best, Jiefei On Mon, Oct 21, 2019 at 12:41 PM varin sacha via R-help <
r-help at r-project.org> wrote:
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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