I'm attempting to implement mixed effects logistic regression in python. As a point of comparison, I'm using the glmer function from the lme4 package in R. I've found that the statsmodels module h...

I want to specify different random effects in a model using nlme::lme (data at the bottom). The random effects are: 1) intercept and position varies over subject; 2) intercept varies over compariso...

I'm looking for suggestions for a strategy of fitting generalized linear mixed-effects models for a relative large data-set. Consider I have data on 8 milllion US basketball passes on about 300 tea...

I have some repeated measures, ordinal response data: dat <- data.frame( id = factor(sample(letters[1:5], 50, replace = T)), response = factor(sample(1:7, 50, replace = T), ordered = T), x1 ...

I am trying to use the Python statsmodels linear mixed effects model to fit a model that has two random intercepts, e.g. two groups. I cannot figure out how to initialize the model so that I can do...

I have a dataset for one year for all employees with individual-level data (e.g. age, gender, promotions, etc.). Each employee is in a team of a certain manager. I have some variables on the team- ...

Example library(glmmTMB) library(ggeffects) ## Zero-inflated negative binomial model (m <- glmmTMB(count ~ spp + mined + (1|site), ziformula=~spp + mined, family=nbinom2, data=Salamanders,...

Suppose we have postulated the following linear mixed model (LMM), which we generically call fit. library(lme4) fit <- lmer(Reaction ~ Days + (1 | Subject), data = sleepstudy) Suppose that we ...

I need to run a logistic regression on a relatively large data frame with 480.000 entries with 3 fixed effect variables. Fixed effect var A has 3233 levels, var B has 2326 levels, var C has 811 lev...

I am using the lme4 package for linear mixed effect modeling The mixed-effect model is: fm01 <- lmer(sublat <- goal + (1|userid)) The above command returns an S4 object called fm01. This obj...

I can use gls() from the nlme package to build mod1 with no random effects. I can then compare mod1 using AIC to mod2 built using lme() which does include a random effect. mod1 = gls(response ~ fi...

I found that the predict function is currently not implemented in cumulative link mixed models fitted using the clmm function in ordinal R package. While predict is implemented for clmm2 in the sam...

How do I fit a ordinal (3 levels), logistic mixed effect model, in R? I guess it would be like a glmer except with three outcome levels. data structure patientid Viral_load Adherence audit_score vi...

I have some high dimensional repeated measures data, and i am interested in fitting random forest model to investigate the suitability and predictive utility of such models. Specifically i am tryin...

In lm and glm models, I use functions coef and confint to achieve the goal: m = lm(resp ~ 0 + var1 + var1:var2) # var1 categorical, var2 continuous coef(m) confint(m) Now I added random effect t...

Is it possible to run a mixed-effects regression model in Spark? (as we can do with lme4 in R, with MixedModels in Julia or with Statsmodels MixedLM in Python). Any example would be great. I've re...

I am a bit confused about the output of Statsmodels Mixedlm and am hoping someone could explain. I have a large dataset of single family homes, including the previous two sale prices/sale dates f...

I'm following this tutorial as well as ?eff_size from package emmeans to compute eff_size() for my regression model below. But I get the error: need an object with call component from the eff_size...

In my R code, I use the MixedModels Julia package. I integrate Julia in R using the JuliaCall package. I work with very large datasets (~1 GB, ~4x10^6 observations) and at the modeling step (mixe...

I can get the significance of pairwise comparisons with the following code m <- lmer(angle ~ recipe*temp + (1|replicate), data=cake) emtrends(m, pairwise~recipe, var="temp") $emtrends recipe ...

I have following model x <- rep(seq(0, 100, by=1), 10) y <- 15 + 2*rnorm(1010, 10, 4)*x + rnorm(1010, 20, 100) id <- NULL for(i in 1:10){ id <- c(id, rep(i,101)) } dtfr <- data.fra...

I am running several linear mixed models for an study about birds with the variable nest as a random variable. The thing is that in some of these models I get what is called 'singular fit': my nest...

I have to run a lmer with a log transformed response variable, a continuous variable as fixed effect and and a nested random effect: first<-lmer(logterrisize~spm + (1|studyarea/teriid), data =...

dput for data, copy from https://pastebin.com/1f7VuBkx (too large to include here) data.frame': 972 obs. of 7 variables: $ data_mTBS : num 20.3 22.7 0 47.8 58.7 ... $ data_tooth: num 1 1 1 1 1 1 ...

I have behavioral data for many groups of birds over 10 days of observation. I wanted to investigate whether there is a temporal pattern in some behaviors (e.g. does mate competition increase over ...