Questions tagged [glmmlasso]

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

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How can I get SEs from predict(glmmLasso )

I am using glmmLasso in R to get predictions; my outcome is binary so I am using a logit model. My code looks something like this: ...
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Interpreting variable selection performance on N datasets using N glmnet LASSOs run with the same lambda of s = 0.1 vs cv.glmnet with s = lambda.1se

This question is a follow-up to this prior question on here from a week ago which was itself a follow-up to this prior question. Here is a link to the GitHub Repository for the project. I am running N ...
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Why are the performance of LASSOs implemented via glmnet in R going down after cross-validation?

This question is a follow-up to this question I asked here last week. I got an important and useful answer to it, but what that led to was surprising. And once again, here is a link to the GitHub Repo ...
Marlen's user avatar
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How to calculate a confidence interval in R for a binomial mixed-effect model (which was fit using the R package glmmLasso)?

How does one calculate confidence intervals for a binomial mixed-effect model that was fit using the R package glmmLasso? I am interested in the 95% confidence intervals for the fixed effects. confint ...
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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 ...
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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 "...
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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.
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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, ...
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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. ...
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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 ...
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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 ...
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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 ...
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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: ...
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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 ...
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`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 ...
Brash Equilibrium's user avatar