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 :
> 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
Hard to help you if you don't provide a reproducible example.
On Sun, Mar 4, 2018 at 1:05 PM, Christien Kerbert <
christienkerb...@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 :
>
>
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 follow
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 i
> On Mar 3, 2018, at 3:04 PM, Christien Kerbert
> 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 #
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 <-
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