Questions tagged [mixed-model]
Mixed (aka multilevel or hierarchical) models are linear models that include both fixed effects and random effects. They are used to model longitudinal or nested data.
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What is the difference between fixed effect, random effect in mixed effect models?
In simple terms, how would you explain (perhaps with simple examples) the difference between fixed effect, random effect in mixed effect models?
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R's lmer cheat sheet
There's a lot of discussion going on on this forum about the proper way to specify various hierarchical models using lmer.
I thought it would be great to have all ...
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Crossed vs nested random effects: how do they differ and how are they specified correctly in lme4?
Here is how I have understood nested vs. crossed random effects:
Nested random effects occur when a lower level factor appears only within a particular level of an upper level factor.
For ...
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How scared should we be about convergence warnings in lme4
If we a re fitting a glmer we may get a warning that tells us the model is finding a hard time to converge...e.g.
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How to choose nlme or lme4 R library for mixed effects models?
I have fit a few mixed effects models (particularly longitudinal models) using lme4 in R but would like to really master the ...
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How to obtain the p-value (check significance) of an effect in a lme4 mixed model?
I use lme4 in R to fit the mixed model
lmer(value~status+(1|experiment)))
where value is continuous, status and experiment are factors, and I get
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What is "restricted maximum likelihood" and when should it be used?
I have read in the abstract of this paper that:
"The maximum likelihood (ML) procedure of Hartley aud Rao is modified by adapting a transformation from Patterson and Thompson which partitions the ...
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When to use generalized estimating equations vs. mixed effects models?
I have been quite happily using mixed effects models for a while now with longitudinal data. I wish I could fit AR relationships in lmer (I think I'm right that I can't do this?) but I don't think it'...
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Unified view on shrinkage: what is the relation (if any) between Stein's paradox, ridge regression, and random effects in mixed models?
Consider the following three phenomena.
Stein's paradox: given some data from multivariate normal distribution in $\mathbb R^n, \: n\ge 3$, sample mean is not a very good estimator of the true mean. ...
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Questions about how random effects are specified in lmer
I recently measured how the meaning of a new word is acquired over repeated exposures (practice: day 1 to day 10) by measuring ERPs (EEGs) when the word was viewed in different contexts. I also ...
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How exactly does a "random effects model" in econometrics relate to mixed models outside of econometrics?
I used to think that "random effects model" in econometrics corresponds to a "mixed model with random intercept" outside of econometrics, but now I am not sure. Does it?
Econometrics uses terms like "...
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What is a difference between random effects-, fixed effects- and marginal model?
I am trying to expand my knowledge of statistics. I come from a physical sciences background with a "recipe based" approach to statistical testing, where we say is it continuous, is it normally ...
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Prediction interval for lmer() mixed effects model in R
I want to get a prediction interval around a prediction from a lmer() model. I have found some discussion about this:
http://rstudio-pubs-static.s3.amazonaws.com/...
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Using lmer for repeated-measures linear mixed-effect model
EDIT 2: I originally thought I needed to run a two-factor ANOVA with repeated measures on one factor, but I now think a linear mixed-effect model will work better for my data. I think I nearly know ...
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Negative values for AIC in General Mixed Model [duplicate]
I'm trying to select the best model by the AIC in the General Mixed Model test. The best model is the model with the lowest AIC, but all my AIC's are negative!
So is the biggest negative AIC the ...
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Mixed Effects Model with Nesting
I have data collected from an experiment organized as follows:
Two sites, each with 30 trees. 15 are treated, 15 are control at each site. From each tree, we sample three pieces of the stem, and ...
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Negative values for AICc (corrected Akaike Information Criterion)
I have calculated AIC and AICc to compare two general linear mixed models; The AICs are positive with model 1 having a lower AIC than model 2. However, the values for AICc are both negative (model 1 ...
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What is the minimum recommended number of groups for a random effects factor?
I'm using a mixed model in R (lme4) to analyze some repeated measures data. I have a response variable (fiber content of feces) ...
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How can I test whether a random effect is significant?
I am trying to understand when to use a random effect and when it is unnecessary. Ive been told a rule of thumb is if you have 4 or more groups/individuals which I do (15 individual moose). Some of ...
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"Model failed to converge" warning in lmer()
With the following dataset, I wanted to see if the response (effect) changes with regard to sites, season, duration, and their interactions. Some online forums on statistics suggested me to go on with ...
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How trustworthy are the confidence intervals for lmer objects through effects package?
Effects package provides a very fast and convenient way for plotting linear mixed effect model results obtained through lme4 ...
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Are mixed models useful as predictive models?
I am a bit confused about advantages of mixed models in regard to predictive modelling. Since predictive models are usually meant to predict values of previously unknown observations then it seems ...
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What is compound symmetry in plain english?
I recently realized that a mixed-model with only subject as a random factor and the other factors as fixed factors is equivalent to an ANOVA when setting the correlational structure of the mixed model ...
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What does the anova() command do with a lmer model object?
Hopefully this is a question that someone here can answer for me on the nature of decomposing sums of squares from a mixed-effects model fit with lmer (from the ...
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How do I interpret the 'correlations of fixed effects' in my glmer output?
I have the following output:
...
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Difference between generalized linear models & generalized linear mixed models
I am wondering what the differences are between mixed and unmixed GLMs. For instance, in SPSS the drop down menu allows users to fit either:
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Under what conditions should one use multilevel/hierarchical analysis?
Under which conditions should someone consider using multilevel/hierarchical analysis as opposed to more basic/traditional analyses (e.g., ANOVA, OLS regression, etc.)? Are there any situations in ...
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What is the difference between generalized estimating equations and GLMM?
I'm running a GEE on 3-level unbalanced data, using a logit link. How does this differ (in terms of the conclusions I can draw and the meaning of the coefficients) from a GLM with mixed effects (GLMM)...
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How to interpret variance and correlation of random effects in a mixed-effects model?
I hope you all don't mind this question, but I need help interpreting output for a linear mixed effects model output I've been trying to learn to do in R. I am new to longitudinal data analysis and ...
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What are easy to interpret, goodness of fit measures for linear mixed effects models?
I am currently using the R package lme4.
I am using a linear mixed effects models with random effects:
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Checking assumptions lmer/lme mixed models in R
I ran a repeated design whereby I tested 30 males and 30 females across three different tasks. I want to understand how the behaviour of males and females is different and how that depends on the task....
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predict() Function for lmer Mixed Effects Models
The problem:
I have read in other posts that predict is not available for mixed effects lmer {lme4} models in [R].
I tried ...
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Satterthwaite vs. Kenward-Roger approximations for the degrees of freedom in mixed models
The lmerTest package provides an anova() function for linear mixed models with optionally Satterthwaite's (default) or Kenward-...
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How can I include random effects (or repeated measures) into a randomForest
I'm not even sure that the question makes much sense, but I think I saw a couple of titles of papers where they proposed random forest with random effects. Is this possible in R?
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Modelling longitudinal data where the effect of time varies in functional form between individuals
Context:
Imagine you had a longitudinal study which measured a dependent variable (DV) once a week for 20 weeks on 200 participants. Although I'm interested in general, typical DVs that I'm thinking ...
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Multiple comparisons on a mixed effects model
I am trying to analyse some data using a mixed effect model. The data I collected represent the weight of some young animals of different genotype over time.
I am using the approach proposed here:
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Why do I get zero variance of a random effect in my mixed model, despite some variation in the data?
We’ve run a mixed effects logistic regression using the following syntax;
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lme and lmer comparison
I was wondering if anyone could enlighten me on the current differences between these two functions. I found the following question: How to choose nlme or lme4 R library for mixed effects models?, but ...
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How to get an "overall" p-value and effect size for a categorical factor in a mixed model (lme4)?
I would like to get a p-value and an effect size of an independent categorical variable (with several levels) -- that is "overall" and not for each level separately, as is the normal output from ...
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REML or ML to compare two mixed effects models with differing fixed effects, but with the same random effect?
Background: Note: My data set and R code are included below text
I wish to use AIC to compare two mixed effects models generated using the lme4 package in R. Each ...
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How do I fit a multilevel model for over-dispersed poisson outcomes?
I want to fit a multilevel GLMM with a Poisson distribution (with over-dispersion) using R. At the moment I am using lme4 but I noticed that recently the ...
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Specifying multiple (separate) random effects in lme [closed]
I was working in R packages nlme and lme4, trying to specify the models with multiple random effects. I found, that only nlme allows to specify the heterogeneous structure of the variance. Therefore, ...
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How to test and avoid multicollinearity in mixed linear model?
I am currently running some mixed effect linear models.
I am using the package "lme4" in R.
My models take the form:
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Variance on the sum of predicted values from a mixed effect model on a timeseries
I have a mixed effect model (in fact a generalized additive mixed model) that gives me predictions for a timeseries. To counter the autocorrelation, I use a corCAR1 model, given the fact I have ...
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