[R] lmrob gives NA coefficients

[Eric Berger]

Hard to help you if you don't provide a reproducible example.

On Sun, Mar 4, 2018 at 1:05 PM, Christien Kerbert <
christienkerbert at gmail.com> wrote:

> 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 <ericjberger at gmail.com>:
>
>> 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
>>> 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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