# [R] Same results but different functions ?

Michael Dewey ||@t@ @end|ng |rom dewey@myzen@co@uk
Mon Mar 23 14:45:44 CET 2020

```The documentation suggests that the rlm method for a formula does not
have psi as a parameter. Perhaps try using the method for a matrix x and
a vector y.

Michael

On 23/03/2020 12:39, varin sacha via R-help wrote:
> Dear R-experts,
>
> The rlm command in the MASS package command implements several versions of robust regression, for example the Huber and the Tukey (bisquare weighting function) estimators.
> In my R code here below I try to get the Tukey (bisquare weighting function) estimation, R gives me an error message : Error in statistic(data, original, ...) : unused argument (psi = psi.bisquare)
> If I cancel psi=psi.bisquare my code is working but IMHO I will get the Huber estimation and not the Tukey. So how can I get the Tukey ? Many thanks for your help.
>
>
> # # # # # # # # # # # # # # # # # # # # # # # #
> install.packages( "boot",dependencies=TRUE )
> install.packages( "MASS",dependencies=TRUE  )
> library(boot)
> library(MASS)
>
> n<-50
> b<-runif(n, 0, 5)
> z <- rnorm(n, 2, 3)
> a <- runif(n, 0, 5)
>
> y_model<- 0.1*b - 0.5 * z - a + 10
> y_obs <- y_model +c( rnorm(n*0.9, 0, 0.1), rnorm(n*0.1, 0, 0.5) )
> df<-data.frame(b,z,a,y_obs)
>
>   # function to obtain MSE
>   MSE <- function(data, indices, formula) {
>      d <- data[indices, ] # allows boot to select sample
>      fit <- rlm(formula, data = d)
>      ypred <- predict(fit)
>      d[["y_obs "]] <-y_obs
>      mean((d[["y_obs"]]-ypred)^2)
>   }
>
>   # Make the results reproducible
>   set.seed(1234)
>
>   # bootstrapping with 600 replications
>   results <- boot(data = df, statistic = MSE,
>                    R = 600, formula = y_obs ~ b+z+a, psi = psi.bisquare)
>
> str(results)
>
> boot.ci(results, type="bca" )
> # # # # # # # # # # # # # # # # # # # # # # # # #
>
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