Thanks for your reply.
I use mvrnorm from the *MASS* package and lmrob from the *robustbase*
package.
To further explain my data generating process, the idea is as follows. The
explanatory variables are generated my a multivariate normal distribution
where the covariance matrix of the variables is defined by Sigma in my
code, with ones on the diagonal and rho = 0.15 on the non-diagonal. Then y
is created by y = 1 - 2*x1 + 3*x3 + 4*x4 + error and the error term is
standard normal distributed.
Hope this helps.
Regards,
Christien
In this section, we provide a simulation study to illustrate the
performance of four estimators, the (GLS), S, MM and MM ridge estimator for
SUR model. This simulation process is executed to generate data for the
following equation Where In this simulation, we set the initial value
for β= [1,2,3] for k=3. The explanatory variables are generated by
multivariate normal distribution MNNk=3 (0,∑x) where diag(∑x)=1,
off-diag(∑x)= ρX= 0.15 for low interdependency and ρx= 0.70 for high
interdependency. Where ρx is correlation between explanatory variables. We
chose two sample size 25 for small sample and 100 for large sample. The
specific error in equations μi, i=1,2,…..,n, we generated by MVNk=3 (0,
∑ε), ∑ε the variance covariance matrix of errors, diag(∑ε)= 1,
off-diag(∑ε)= ρε= 0.15. To investigate the robustness of the estimators
against outliers, we chosen different percentages of outliers ( 20%, 45%).
We choose shrink parameter in (12) by minimize the new robust Cross
Validation (CVMM) criterion which avoided
2018-03-04 0:52 GMT+01:00 David Winsemius <dwinsem...@comcast.net>:
>
> > On Mar 3, 2018, at 3:04 PM, Christien Kerbert <
> christienkerb...@gmail.com> wrote:
> >
> > Dear list members,
> >
> > I want to perform an MM-regression. This seems an easy task using the
> > function lmrob(), however, this function provides me with NA
> coefficients.
> > My data generating process is as follows:
> >
> > rho <- 0.15 # low interdependency
> > Sigma <- matrix(rho, d, d); diag(Sigma) <- 1
> > x.clean <- mvrnorm(n, rep(0,d), Sigma)
>
> Which package are you using for mvrnorm?
>
> > beta <- c(1.0, 2.0, 3.0, 4.0)
> > error <- rnorm(n = n, mean = 0, sd = 1)
> > y <- as.data.frame(beta[1]*rep(1, n) + beta[2]*x.clean[,1] +
> > beta[3]*x.clean[,2] + beta[4]*x.clean[,3] + error)
> > xy.clean <- cbind(x.clean, y)
> > colnames(xy.clean) <- c("x1", "x2", "x3", "y")
> >
> > Then, I pass the following formula to lmrob: f <- y ~ x1 + x2 + x3
> >
> > Finally, I run lmrob: lmrob(f, data = data, cov = ".vcov.w")
> > and this results in NA coefficients.
>
> It would also be more courteous to specify the package where you are
> getting lmrob.
>
> >
> > It would be great if anyone can help me out. Thanks in advance.
> >
> > Regards,
> > Christien
> >
> > [[alternative HTML version deleted]]
>
> This is a plain text mailing list although it doesn't seem to have created
> problems this time.
>
> >
> > ______________________________________________
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
> David Winsemius
> Alameda, CA, USA
>
> 'Any technology distinguishable from magic is insufficiently advanced.'
> -Gehm's Corollary to Clarke's Third Law
>
>
>
>
>
>
[[alternative HTML version deleted]]
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