[R] lmrob gives NA coefficients
Eric Berger
ericjberger at gmail.com
Sun Mar 4 11:30:53 CET 2018
What is 'd'? What is 'n'?
On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert <
christienkerbert at gmail.com> wrote:
> 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 <dwinsemius at comcast.net>:
>
> >
> > > On Mar 3, 2018, at 3:04 PM, Christien Kerbert <
> > christienkerbert at 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 at 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]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
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> PLEASE do read the posting guide http://www.R-project.org/
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> and provide commented, minimal, self-contained, reproducible code.
>
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