"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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Need help selecting appropriate statistical method for animal study

I could use some help deciding on the proper statistical method for a current experiment. The experiment is setup as follows: Independent Variable: Diet (4 groups of 10 for a total of 40 animals) ...
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Mixed effects model for repeated measures to test for factors that are either constant or dynamic within an individual over time

I am dealing with a rather complicated dataset with repeated measures of the same individuals at various time points (samples were collected at different time points and different number of samples ...
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marginal R2 = conditional R2 in mixed model

Is it possible that the marginal and conditional r squared are the same in a mixed model? I get that situation a few times when adding a spatial autocorrelation structure to the model. Without this ...
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Longitudinal data analysis

I have a question regarding longitudinal study analysis. I tried to search similar questions like mine but didn't really find it. So here is the brief description of data and my question: I have a ...
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11 views

Non-nested model with uniquely identifying groups

I'm testing various specifications of linear mixed effects models with lmer() in R. The data are fiscal year firm-level, so ...
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9 views

What's the drawback of using interaction terms to analyze the pre-post control data?

I am trying to analyze the data with the pre-post-control design in the context of RNA-seq analysis. I have read Best practice when analyzing pre-post treatment-control designs, but I am still ...
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Structural zero design in mixed effects model

I would like to do a mixed effects regression that is like this: ISI ~ Location + Stage + Stage*Location + 1|Patient/Chan Where Location and Stage are fixed ...
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14 views

Distinction between fixed effects and random effects for continuous predictors

The distinction between fixed effects and random effects seems intuitively clear to me. A factor is a fixed effect if the set of possible levels for the factors is fixed. A fixed effect factor would ...
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27 views

Mixed-effects in SAS

I am analyzing weekly data on 50 products which were sold in a number of shops during one year. My goal is to estimate a mixed-effects model for unit sales with heterogenous AR(1) error structure. ...
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Notation of MCMCglmm for multinomial multilevel models

I want to estimate a multilevel multinomial logit model but I am struggling with the terminology and notation used by the R-package MCMCglmm. There is documentation ...
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19 views

Different results in a mixed model when compared with raw data

I ran a model with reaction time as my DV and PWI Condition (2 levels) as one of the fixed factors. I used contr.sum for all fixed factors. I ran the following model to look for differences in ...
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Interpretation of deviation coded data in mixed effects models

ran a model with reaction time as my DV and PWI Condition as one of the fixed factors. I used contr.sum for all fixed factors. I ran the following model to look for differences in reaction time ...
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25 views

reporting mixed effects linear regression: t statistic or model comparison?

We have a one factor three level repeated measure experiment with ratio data (reaction times). We fit a mixed effects linear model using lmer (in fact lmerTest) - maximal, with subjects and items as ...
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Binomial Temporal GAMM does not converge (R::mgcv)

I am new to both mixed effect and Additive models so I'm sorry if the answer here is trivial. I have data collected on several metabolic chemicals (M1,M2...), covariates (time,Race,Gender...) and ...
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21 views

Mixed effect modeling SPSS: within-individual

I’m new to mixed effect modeling in SPSS and wonder if anyone could assist me with the analysis: I have longitudinal data from one country as follows: For 20 time points [years] I have the average ...
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17 views

Pseudoreplications and the methodes used to explore the correlations

In my experiment I have measured growth of different trees on predefined circular plots (x, y, z, a). On each plot all trees were measured. For each location I have one treatment information. Now ...
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16 views

Effects of covariance structures on mixed effects models

What are generally the effects of using a covariance structure on a mixed effect model ? More specifically, in a mixed model, what should be the expected effect of using an AR(1) covariance ...
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61 views

How to backtransform data that has been log transformed in order to report raw values for ease of interpretation?

I have run some lme4 analyses on reaction time data in R, with RT being the main outcome variable of interest, which I first log transformed due to non-normality ...
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61 views

Lmer v. lm with Dummy Variables. Where Does the Math Differ in a Simple Example?

I am trying to understand the concept of mixed effect along with the R syntax in lme4 with simple scenarios out of the dataset ...
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48 views

lme4: Why won't lsmeans output my fixed effects?

I'm trying to plot confidence intervals for linear mixed effects models trained with lme4 and lmerTest in R. I am using this data file, which I've shared via Google Drive. Here is my trained model. ...
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Mixed-modeling when no observation contributes both X and Y

I'm working on a project investigating the relationship between (let's say) a face's perceived masculinity and its perceived competence. There was a large number of face stimuli (80). Two completely ...
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44 views

Random slope and random intercept correlation at every level of X

Lets say individuals are nested within each ID and I am trying to a predict level 1 outcome Y from a level 1 predictor X1 or X2 with random slopes and intercepts. X1 and X2 are equivalent to each ...
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2answers
47 views

Need help with nested random/mixed effect model specification

I am a newbie in meta-analysis and I need your opinion on the design of my random-effect model. I have conducted an experiment on the performance of a provider who has around 30-40 data centres. I ...
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1answer
56 views

calculating adjusted means from lmer

How can I calculate adjusted means for a regression model with fixed and random effects? I'd like to calculate the adjusted means for a lme regression with this formula ...
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17 views

Mixed Effects Poisson on error data

I have data from a 2 x 2 repeated measures factorial experiment. There are 20 participants who each completed 24 trials for each one of the following factor combinations. 1) A1 x B1 2) A1 x B2 3) ...
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64 views

Mixed effects model: model fitting vs conceptual sense

I have a data from a 2 (load) x 2 (comp) x 2 (sal) full factorial repeated measures experiment and I'm trying to fit a linear mixed effects model to it. Here is a sample of the data: ...
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Understanding variance estimation by restricted maximum likelihood (REML)

I'm reading Doug Bates' theory paper on R's lme4 package to better understand the nitty-gritty of mixed models, and came across an intriguing result that I'd like to understand better, about using ...
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112 views

Mixed effects model hypothesis testing

I ran a 2 x 2 x 2 full factorial repeated measures experiment where 20 participants were exposed 30 times to all combinations of the factors A, B and C in random order. This is a standard procedure in ...
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“Pairwise not statistically different” leads to “overall statistically different”?

I have a linear mixed-effect model $$ y=\beta_0+\beta_1x_1+\beta_2x_2+\beta_3x_3+Zb+e, $$ where $[x_1\ x_2\ x_3]$ represents the fixed effects, and $Z$ represents the random effects. Now, I test the ...
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33 views

Testing outlier influence on random effects in linear mixed effects models

I have been reading a little bit about diagnostics for linear mixed effects models and have started wondering about how outliers may influence random effects in addition to fixed effects. The paper on ...
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41 views

Repeated measures through time using mixed effects in R, plus post hoc tests

I have been trying to figure out how to do a fairly basic repeated measures analysis using linear mixed effects in R, and then analysing it using post-hoc tests. The problem is that I'm not sure ...
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What's an example of a situation in which it makes sense to assume random slopes but a fixed intercept?

I'm referring to multilevel modelling. Field (2013) writes: It’s worth noting that it would be unusual in reality to assume random slopes without also assuming random intercepts, because ...
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18 views

GEE Logistic Model with Subject Specific Predictions?

I have fit a marginal logistic model or GEE Logistic Regression model using SAS' proc genmod to obtain estimated parameters associated with mortality (death). Using SAS, I am able to obtain ...
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38 views

Rule of thumb for sample size for mixed-effects logistic regression analysis?

Is there a simple way of calculating the minimum number of participants (and/or items) needed for a mixed-effects logistic regression analysis? In particular, what should I do if I don't know what to ...
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shall I add all correlated variables in a mixed effect model?

If I have a mixed effect model case with various predictor variables. If some of the variables are correlated; is it better to add both correlated variables to the model? or shall i take out one of ...
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I'm having trouble fitting a general (not generalized…) linear mixed-effect model using the lme4 package. Can anybody help?

I need to fit a linear mixed model in the "Laird and Ware" framework. This type of model is usually specified by (as you may know): $\mathbf{y}_i = X_i \beta + Z_i \mathbf{b}_i + \mathbf{\epsilon}_i ...
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17 views

Intra-individual covariance matrix in lme4

I wonder, why do not need to specify the structure of the variance covariance matrix and the lmer function library lme4, since using the lm function library nlme this is possible. Thank you.
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correlated variables as fixed effect in mixed effect models

I am interested to know whether the count of beetles depends more on precipitation or in minimum temperature in winter. I currently model as fixed effects: altitude (correlated with temperature) ...
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33 views

Should I include weights in LME?

I have two case studies where I am looking at the influence of a trait (trait A) on mortality (m) of trees and seedlings. Following your comments on ...
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How to analyse interbirth interval data with 0s

OK.. So to give a bit of context, this is what my data looks like: My question is regarding the last variable column (IBI). This records the interbirth interval in days between each birth (the date ...
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34 views

Mixed models and longitudinal studies: Is it ok to specify a random slope with time as a categorical?

My model is currently setup as follows either with just random intercepts: ...
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28 views

Variance component model for longitudinal data

I have a dataset with fixed and random effects, sampled over time (body phenotypes under fixed stimulations). Generally speaking, I'd like to construct a variance component/ partitioning model to ...
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94 views

When the dependent variable and random effects 'overlap' in mixed effects models

I have added a new example here for clarity, see original question below Eg. I have 10 schools in 5 countries, ten students from each school is sampled. Prediction variables: student test marks for ...
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129 views

Post-hoc testing in multcomp::glht for mixed-effects models (lme4) with interactions

I am new to CrossValidated, so if there is a better way for me to format or ask my question, please feel free to comment. I am performing post-hoc tests on a linear mixed-effects model in R (package ...
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86 views

95% CI in nonlinear mixed-effect model {lme4} with two or more crossed random effects

I have fisheries-independent data and am interesting in estimating maturity patterns across 50 lakes that are sampled (with bias) by 4 types of gear-collections. The sampling pattern is very ...
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Nested linear mixed-effects model

I have 9 sites. Within each site, plant life was sampled to represent 70% of the basal area. Of the sampled plants, I know the corresponding family, genus, and species. For this project, I extracted ...
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58 views

Negative binomial mixed effect model for repeated measures with R - prediction and plotting

I have a dataset to analyze in which a response was recorded at the ends of months 1,3,4,5,6 in 187 patients. All patients had the responses recorded in each week, and all patients started a treatment ...
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17 views

Nesting v. interaction in LMM

I have a continuous response variable, a continuous predictor (P1) and a variable (elevation, P2) that could be treated either as continuous or categorical (I guess?). I also have an ID as the random ...
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Mixed-effect model design with a sampling variable

I am trying to specify a formula for a linear mixed effect model (with lme4) for my experimental design, but I'm not sure I'm doing it right. The design: basically ...
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Hypothesis testing: If not a p-value in mixed effect models, then what?

I've been working on a messy, repeated measures data set of endocrine data looking at a small group of variables (after eliminating several uninteresting contenders in exploratory data analysis), each ...