Questions tagged [glmmlasso]

glmmLasso is an R package that implements LASSO-regularized generalized linear mixed models.

Filter by
Sorted by
Tagged with
0
votes
0answers
4 views

glmmLasso for nested design

Hi would like to build prediction model for my nested design data. I have 96 plots and 24 clusters (4 plots for each cluster) which I calculated 10 vegetation variables. I would like to use Poisson ...
3
votes
1answer
54 views

LASSO versus likelihood ratio tests for variable selection

LASSO regression penalizes coefficients in regression to at most zero. Likelihood ratio tests tells us whether the nested or full model is better. I used likelihood ratio tests during regression ...
0
votes
0answers
53 views

Random Slopes and Starting Parameters with GLMMLASSO

I am using glmmlasso in a simulation study. I want to decrease the time it takes to select the tuning parameter, lambda, by using the technique described in this answer: https://stats.stackexchange....
0
votes
0answers
38 views

How to give a represantation to veriables from each group using LASSO

I'm trying to apply LASSO regression on my data set in order to choose the best variables. However, my variables (44 to be accurate) come from 7 different groups, is there any option to give a "...
-1
votes
1answer
74 views

I have some few question regarding OLS.

Can I interpret my my coefficient's p-values even I violated the error normality assumptions? I have a large sample size.
0
votes
0answers
207 views

Appropriate implementation of function cv.glmnet from glmnet R package regarding binary categorical outcome

I would like to use the cv.glmnet() function, in a microarray dataset to perform some kind of "feature selection/variable importance" and prioritize with this way, ...
4
votes
1answer
637 views

Why under joint least squares direction is it possible for some coefficients to decrease in LARS regression? [duplicate]

I think I understand how LARS regression works. It basically adds features to the model when they are more correlated with the residuals than the current model. ...
2
votes
1answer
588 views

Mixed effects Lasso model setup in R, for high dimensional data

My goal is to model the relationship between RETURN and SCORE from my survey dataset with the following structure: RETURN (numeric continuous) = company share price performance SCORE (numeric ...
2
votes
1answer
1k views

Why is the glmmLasso package failing to add random effects?

I am trying to determine if a problem I'm having with the glmmLasso package in r is caused by my local machine or if it's a ...
2
votes
0answers
690 views

Specification of mixed model structure in glmmLasso

I am having difficulties specifying the appropriate structure for nested/random effects in a mixed model that I am trying to pass through the 'Lasso' shrinkage algorithm. I am using the package ...
1
vote
0answers
157 views

How does glmmlass work for Linear Mixed effect model?

When I used the glmmlasso for a linear mixed model (gaussian), I got a warning message: ...
3
votes
1answer
2k views

Relation between the tuning parameter $\lambda$, parameter estimates $\beta_i$ and constraint $s$ in LASSO logistic regression

In the context of LASSO logistic regression, I understand that $\lambda$ is the tuning parameter obtained by cross validation. There is also the constraint parameter $s$ ($\sum_{i=1}^p|\hat\beta_i|\le ...
1
vote
1answer
305 views

`bGAMM` and other `GMMboost` algorithms for large data sets

Regularized generalized linear mixed models and generalized additive mixed models are exactly what I need. I'm an R user, so it looks like bGAMM and maybe ...