"Mixed effects models" refers to models that have both fixed effects and random effects. They are used to model longitudinal data or data that are clustered & thus do not have independent errors.

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Recommendation for books/notes for linear mixed effect models for longitudinal data?

I'm a beginner in data analysis who needs to learn (say in a period of 2 to 3 weeks or so) the key ideas and techniques in the linear mixed effect models for longitudinal data. I'll apply them in ...
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Not sure whether to include random effect that's related to fixed effects

I'm unsure about whether I need to include a random effect in a mixed effects model that I'm running, as the fixed effects are related to this random effect. I'm looking at how the intelligibility ...
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Can any one give me inf how I can form X, Z, R, G, and A matrices using dummy variables using the posted info below?

Y=Xb +Zu + e, where y represents a vector of observed (measured) phenotype values, b is vector of unknown parameters for “fixed" effects, while X is corresponding design matrix, u is vector of ...
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Probabilities of odds ratios in random intercept models?

I'm using R and the lme4 package to compute mixed effects models with binary outcome (glmer). I have included continuous ...
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Mixed effects models - which are the random parts?

I have data on family care for elderly people. Data stem from 6 EU counries. People were asked at baseline and followed-up one year later. Now I'd like to find predictors that explain why people ...
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Multilevel analysis in R and Stata

I am trying to replicate a multilevel logistic analysis which uses a dyadic time series data set and R. As for my part, I am using the same data set but Stata. The original syntax has the following ...
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interpreting an interaction of two continuous variables in a mixed effects model [duplicate]

I am hoping for some guidance on how to interpret the interaction in a mixed effects model. The interaction is between two continuous variables (EarlyLanguageExposure and NeighborhoodSize). My ...
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Coding of categorical random effects in R: int vs factor

I have a problem with coding of a 2-level categorical predictor variable in R, and subsequently using it as a random slope in lmer(). I can keep the factor as numeric, coded using the treatment ...
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How to report mixed effects logistic regression

I have several models predicting different binary outcomes as a function of time (binary variable: before/after intervention) and age (ranges 4 to 14), measured in different students within different ...
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Proc Mixed for a random slopes model - contrast the slopes?

I have a need to make predictions about a set of students $^1$ who are nested under teachers, under schools, under districts. I have produced the below model, and I now wish to do some forecasting at ...
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Using the same variable as a fixed and random effect in Mixed Effect Models

The experiment this data comes from an experiment where two people collaborate to put objects in a specific order. The Direction has the target array on their screen, and the Matcher has a scrambled ...
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Interpreting a linear mixed effect model's interaction term

I am a biologist and am attempting to analyze the effects of time and location on depth. I was told I needed to use a mixed effects model to account for the random variables of Individual and tracking ...
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Trying to understand the basics of a mixed-effects logistic regression model for a 10-step continuum

I am trying understand how to correctly build a mixed-effects logistic regression model in R. I believe my model is pretty simple and straight forward but I'm lacking in experience and uncertain I'm ...
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Fixed effect vs random effect when all possibilities are included in a mixed effects model

In a mixed effects model the recommendation is to use a fixed effect to estimate a parameter if all possible levels are included (e.g., both males and females). It is further recommended to use a ...
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Mixed effects structure: Main effects in random slopes with between-subjects design

My question is about whether it is appropriate to include certain random slopes in a mixed effects model with a between-subjects design. This is my first question on this stack exchange, and I'm ...
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One observation per level in mixed-effect model

Field explains how to analyse repeated-measures data using linear mixed-effect models (LME). See Field et al., Discovering Statistics Using R, 2012, p. 573. However, the way he specifies the model, ...
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how to fit nlmer non-linear mixed model and have asymptote fixed?

I'm trying to compare the model where asymptote value varies over subject and the one that does not. I've fit the first (the one that varies) but can't seem to figure out the latter. The first one is ...
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What statistical test is performed by summary(glht(model, linfct=mcp(factor=“Tukey”)), test=adjusted(type=“none”))?

I have data that I have fit using lme with the following structure (Subject is implemented as a random effect in order to account for multiple paired comparisons): ...
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Compare linear mixed models with Likelihood ratio tests and significant fixed effects

I am comparing two models with likelihood ratio tests and I have found the -2LL increases with the more complex model (when a 3-way interaction between 3 fixed effects is included). However, the tests ...
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How can I model this problem?

Genetic algorithms are a kind of evolutive approach to problem solving where solutions are randomly generated and crossed with each other as to produce other solutions. With each generation or ...
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Adding a variance structure when fitting a gamm with Gamma distribution

I am using the code below to fit a gamma GAMM introducing a variance structure that informs the model that variance of the response variable is much larger in one of the levels of the factor coast ...
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1answer
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F stats for post hoc test of a linear mixed effects model

I have an unbalanced linear mixed effects model with three fixed factors of various levels and one random factor for my repeated measures data (for details see here). Thanks to your help I managed to ...
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Measurements from two raters. Should I use multilevel/“random-effects” model?

I have several variables that were measured on patients, for most patients the variables of interest were measured by 2 independent "raters". Most of the variables are binary. I need to compute the ...
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Accounting for heteroskedasticity in lme linear mixed model?

I have a data set where I measured the number of molecules (M) present in cells as a function of drug (with or without) and days of treatment (5 timepoints). I repeated the experiment 3 times, with ...
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time varying continuous covariate with one single binary outcome

Despite having only a single binary outcome for each ID, there are multiple correlated measurements for the same test for each ID at different timepoints. The individual ID´s are obviously ...
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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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Two repeated measures treatment analysis. Regression of the delta or mixed effect model?

We want to study the effect of a certain treatment on performance on a test and I would like to have some suggestion from you. We want to use a regression model in order to control for confounders. ...
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Comparing between random effects structures in a linear mixed-effects model

During a recently asked question about linear mixed-effects models I was told that one should not compare between models with different random effects structures using likelihood ratio tests. Up until ...
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Mixed and random effect model with multiple crossed random effects in lme4 vs nlme

I am trying to fit a few models as follows for my data of observations recorded from $p$ genotypes planted in $n$ locations for $m$ years. The aim is to estimate BLUPs finally. $$Y_{ijk} = \mu + G_i ...
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How to report a linear mixed-effects model equation

I have run a linear mixed-effects model, with one fixed effect (dd) and a random slope and intercept term for individual (fInd) and would like to know how to report the results? In particular, I would ...
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Joint Models vs the 'usual' time-dependent Cox regression for time-varying predictors

I've got a methodological question, and no data set attached. Suppose I aim to fit a proportional hazards model (Cox) for survival data. I have multiple observations for each individual (data in long ...
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Testing whether random effects are normally distributed in R

I've been working on a GLMM in R and I see that an assumption of the test is that the random factor must be normally distributed (that is, unless you're using a package like ...
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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 dataset 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 model has one fixed effect and one ...
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Using a mixed effects regression model for between-subject design?

I have data from a between-subject experiment, where every subject was assigned to one of the two conditions, and completed varying number of trials (as much as they wanted). Number of trials is ...
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Using glmer, why is my random effect zero?

We’ve run a mixed effects logistic regression using the following syntax; ...
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The biological significance of ANOVA 3-way interaction

Dear statistics experts, I have trouble to find a sensible statistical approach to back up some very obvious (at least to my eyes) interpretation of a dataset (see descriptive plot below). I measure ...
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Aligned Rank Transform with random factor

In an aligned rank transform preceding a two-way ANOVA, the procedure is: save residuals by performing a standard ANOVA use Aggregate to determine effects for group means (mij for interaction, ai ...
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lme() with several within and between (categorical and continuous) subject factors

I am currently trying to analyse data from an experiment of mine and I have done some searching for instructions on the usage of the lme() function for R, since I am looking to analyse my data with a ...
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Interpreting the random effect in a mixed-effect model

I am looking at several dependant variables for which I created LMMs of the following kind: DV ~ Group + (1|Subject) + (1|Time) Now I am struggling with how to ...
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Reporting Fixed Effects as (partial) correlations?

I'm doing a linear mixed effects analysis in which I'm really only interested in one of the fixed effects. I have several other fixed effects and a random intercept term, but none of them are ...
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Random effect with zero SD in LMM

In my mixed-effects model there are one fixed effect and two random effects (subject and time of measurement). fit <- lmer(DV ~ group + (1|subject) + (1|time)) ...
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Likelihood and estimates for mixed effects Logistic regression

First let's simulate some data for a logistic regression with fixed and random parts: ...
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How to nest hierarchical data variances in R

Apologies ahead of time for not having an exact data set as this is more of theoretical question that I stumbled across while working on mixed effects models. Suppose I have the following data ...
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Fitting mixed effects logistic regression with random effects

I have a data frame of 134 observations, 9 independent variables, and a binary, categorical response; please see its structure below: ...
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Why and how does the inclusion of random effects in mixed models influence the fixed-effect intercept term?

The question is best illustrated by this example which uses a dataset (in library faraway) and lme4 library (both in R). This ...
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Mixed effect linear regression model output interpretation

I just fitted the following linear mixed effects model: ...
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Number of observations in groups - linear mixed effects model

I would like to fit linear mixed effects model to my dataset, but I was wondering if quantity of observations in groups matter? I have some groups with about 60 observations in each, but there are ...
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Appropriate df for a linear mixed model when looking at variance

Suppose I fit a linear mixed model as: lme(Response ~ 1 , random = ~1| Location | User | Machine). Thus machine is nested within user which is nested within ...
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How to compare different nlme models ?

If there are 2 nlme models with same non-linear mean function, model 1 and model 2, how do you compare them ? Which R function does this for us ? And when there are random effects or fixed effects, I ...