[R] Simulations study not working entirely...

[Wang Jiefei]

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 using 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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