Thank you for the input Brian and Ben. It is odd how it seems to handle a two way interaction fine (as long as the continuous variable is not in the mix), but not a 3-way.
In any case would anyone be able to give me a rundown of how I would create a matrix/dummy variable for these interactions to input into glmmLASSO? Alternatively, is there a method for paring down a model that is a bit less sketchy than simple backfitting, that you would expect to be more straight forward software-wise? Thanks! Walker UW-MKE On Thu, Jul 14, 2016 at 10:08 AM, Cade, Brian <ca...@usgs.gov> wrote: > 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: ca...@usgs.gov > tel: 970 226-9326 > > > On Wed, Jul 13, 2016 at 2:20 PM, Walker Pedersen <w...@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 >> >> ______________________________________________ >> 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. > > ______________________________________________ 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.
