Hi Varin,
I did not look inside your code yet but I have a few suggestions. First I
think your problem should be described in more detail, just saying you have
a problem is not enough for us to diagnose. Second Your example depends on
too many other packages and I'm not sure if you need all of them to
reproduce the error. A minimum example will be appreciated. finally, if
this is a package problem as you said, it might be better to ask the
question in https://github.com/kloke/hbrfit/issues since the author
definitely knows the context more than us and might be able to provide a
solution for your question.
Best,
Jiefei
On Mon, Oct 21, 2019 at 12:41 PM varin sacha via R-help <
r-help@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
>
> ###############
>
> ______________________________________________
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
>
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