[R] glmmLasso with interactions errors

[Cade, Brian]

It has never been obvious to me that the lasso approach can handle
interactions among predictor variables well at all.  I'ld be curious to see
what others think and what you learn.

Brian

Brian S. Cade, PhD

U. S. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO  80526-8818

email:  cadeb at usgs.gov <brian_cade at usgs.gov>
tel:  970 226-9326


On Wed, Jul 13, 2016 at 2:20 PM, Walker Pedersen <wsp at uwm.edu> wrote:

> Hi Everyone,
>
> I am having trouble running glmmLasso.
>
> An abbreviated version of my dataset is here:
>
> https://drive.google.com/open?id=0B_LliPDGUoZbVVFQS2VOV3hGN3c
>
> Activity is a measure of brain activity, Novelty and Valence are
> categorical variables coding the type of stimulus used to elicit the
> response, ROI is a categorical variable coding three regions of the
> brain that we have sampled this activity from, and STAIt is a
> continuous measure representing degree of a specific personality trait
> of the subjects. Subject is an ID number for the individuals the data
> was sampled from.
>
> Before glmmLasso I am running:
>
> KNov$Subject <- factor(KNov$Subject)
>
> to ensure the subject ID is not treated as a continuous variable.
>
> If I run:
>
> glm1 <- glmmLasso(Activity~as.factor(Novelty) + as.factor(Valence) +
> STAIt + as.factor(ROI)
> + as.factor(Valence):as.factor(ROI), list(Subject=~1), data = KNov,
> lambda=10)
> summary(glm1)
>
> I don't get any warning messages, but the output contains b estimates
> only, no SE or p-values.
>
> If I try to include a 3-way interaction, such as:
>
> glm2 <- glmmLasso(Activity~as.factor(Novelty) + as.factor(Valence) +
> STAIt + as.factor(ROI)
> + as.factor(Novelty):as.factor(Valence):as.factor(ROI),
> list(Subject=~1), data = Nov7T, lambda=10)
> summary(glm2)
>
> I get the warnings:
>
> Warning messages:
> 1: In split.default((1:ncol(X))[-inotpen.which], ipen) :
>   data length is not a multiple of split variable
> 2: In lambda_vec * sqrt(block2) :
>   longer object length is not a multiple of shorter object length
>
> And again, I do get parameter estimates, and no SE or p-values.
>
> If I include my continuous variable in any interaction, such as:
>
> glm3 <- glmmLasso(Activity~as.factor(Novelty) + as.factor(Valence) +
> STAIt + as.factor(ROI)
> + as.factor(Valence):as.factor(ROI) + as.factor(Novelty):STAIt,
> list(Subject=~1), data = Nov7T, lambda=10)
> summary(glm3)
>
> I get the error message:
>
> Error in rep(control$index[i], length.fac) : invalid 'times' argument
>
> and no output.
>
> If anyone has an input as to (1) why I am not getting SE or p-values
> in my outputs (2) the meaning of there warnings I get when I include a
> 3-way variable, and if they are something to worry about, how to fix
> them and (3) how to fix the error message I get when I include my
> continuous factor in an interatction, I would be very appreciative.
>
> Thanks!
>
> Walker
>
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