d is the number of observed variables (d = 3 in this example). n is the
number of observations.
2018-03-04 11:30 GMT+01:00 Eric Berger <ericjber...@gmail.com>:
> What is 'd'? What is 'n'?
>
>
> On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert <
> christienkerb...@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 <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]]
>>
>> ______________________________________________
>> 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/posti
>> ng-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
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