Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [glmmlasso]

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

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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 analysis to figure out how to make the most…
user271077
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Why under joint least squares direction is it possible for some coefficients to decrease in LARS regression?

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. And then, after adding the features to the model, it will increase the coefficients in…
makansij
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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 s $). How this constraining parameter $s$ is…
Tyrone Williams
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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 throws "no applicable method for 'vcov' applied…
Gabriel
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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 continuous) = company score collected via…
Steve.B
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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 larger problem. When I run the examples give in the glmmLasso documentation, the random effect structure is not incorporated…
Adam C
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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 glmmLasso. My data consists of disease incidence data…
johnybinwv
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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: 1: In log((1 - mu)[y == 0]) : NaNs produced 2: In nlminb(1e-16, taylor.opt, y = y, X = Z_alles, fixef = Delta_start[1:lin], ... : NA/NaN function…
Shuguang
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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 glmmLASSO are possibilities outside of doing my own implementation (which I don't have time to…
Brash Equilibrium
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Not meaningful for factorsError in glmmLasso

I am experiencing this error not meaningful for factorsError in if (xi > xj) 1L else -1L : missing value where TRUE/FALSE needed when trying to fit a lasso mixed model based on the R package glmmLasso and I can't quite figure out what's causing…
Myriad
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How can I get p-values from a regularized GLMM?

I have a dataset containing information about patients in a hospital, with the following variables: Status for a certain disease (binary outcome) Hundreds of continuous biomarkers A few variables for adjustment (age, gender, etc.) Patient ID My…
PedroSebe
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Why does order of predictor changes the results of glmmLasso

I recently noticed that depending on the order I write the predictors for in a lasso based mixed model as implemented in glmmlasso in R documented here I get different coefficients, z, and p values which complicates interpretation. I am wondering if…
Myriad
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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 "representation" to each group?
Erik
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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, the selected features with the lasso approach (which coefficients are different than…
Jason
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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.
Nikko Joe Ramal
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