CODEWITHSUNDEEP
rmixed-models
xralphyx
xralphyx

Reputation: 43

glmmLasso Warning Messages

I am trying to run glmmLasso to estimate a mixed model with the command:

glm1_final <- glmmLasso(Activity~Novelty + Valence + ROI, rnd = 
list(Subject=~1), data = KNov, lambda=lambda[opt],switch.NR=F,final.re=TRUE)

This code is basically taken from demo("glmmLasso-soccer"), but with my variables substituted in. Activity is a measure of brain activity, Novelty and Valence are categorical variables coding the type of stimulus used to elicit the response and ROI is a categorical variable coding three regions of the brain that we have sampled this activity from. Subject is an ID number for the individuals the data was sampled from (n=94). lambda[opt] is being set in a previous step, although that previous step is giving me the errors in question as well, so I don't know if it is accurate.

I keep getting two warnings:

Warning messages: 1: In split.default((1:ncol(X))[-inotpen.which], ipen) : data length is not a multiple of split variable 2: In est.glmmLasso.RE(fix = fix, rnd = rnd, data = data, lambda = lambda, : Cluster variable should be specified as a factor variable!

The first only occurs if ROI is in the model. I haven't identified any change I can make to the model to make the second go away.

I have no idea what these warnings mean, and google hasn't turned up anything on them. I do still get estimates for my parameters, I just don't know if they are accurate, since I don't know what the warnings mean.

Anyone know what these warnings mean?

UPDATE:

I have uploaded an abreviated version of my data to: Google Drive

I have confirmed I still get the second error if I run the code:

KNov <- read.table("Nov_abr.txt", header = TRUE)
KNov$Subject <- factor(KNov$Subject)
library(glmmLasso)
glmmLasso(Activity~Novelty + Valence + ROI, rnd = list(Subject=~1), data = KNov, lambda=10,switch.NR=F,final.re=TRUE)

The KNov$Subject <- factor(KNov$Subject) did clear up the other error.

The version of R I have is: R version 3.3.0 (2016-05-03), Platform: "i386-w64-mingw32"

Upvotes: 2

Views: 2723

Answers (1)

Ben Bolker
Ben Bolker

Reputation: 226522

You should use

KNov$Subject <- factor(KNov$Subject)

to get rid of the first warning, and as.factor(ROI) in your fixed-effect formula, as documented (emphasis added below):

fix: a two-sided linear formula object describing the fixed-effects part of the model, with the response on the left of a ‘~’ operator and the terms, separated by ‘+’ operators, on the right. For categorical covariables use ‘as.factor(.)’ in the formula. Note, that the corresponding dummies are treated as a group and are updated blockwise

So

glmmLasso(Activity~Novelty + Valence + as.factor(ROI),
      rnd = list(Subject=~1),
      data = KNov, lambda=10,switch.NR=F,final.re=TRUE)

seems to work (I still get a warning, but I think that's because I'm using a tiny subset of the data). (This syntax is also illustrated in the"linear mixed model with categorical covariates" example in ?glmmLasso.) Yes, it would be nice to get a more explicit warning message, but the answer is in the documentation ...

Upvotes: 5

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