Parameters associated with the particular levels of a covariate are sometimes called the “effects” of the levels. If the levels that are observed represent a random sample from the set of all possible levels we call these effects "random."

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26 views

Random Effects Model - composite error

In a random effects model, the composite error is defined as $\epsilon_{it} = \alpha_{i} + u_{it}$ where $\alpha_{i}$ is uncorrelated with $u_{it}$; the $u_{it}$ have constant variance and are ...
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11 views

Marginal effect calculation after logistic regression with panel dataset using R

I would like to perform a logit regression with a panel dataset, I know that the pglm package does the job, however, does anyone know if there is a standard package in R that allows me to calculate ...
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4 views

fitting behavioral data using glmmADMB while accounting for 2 repeated measured structures

I am attempting to analyze an experiment where I am testing for differences in agonistic behaviors (e.g. bite etc...) between two morphs of frogs sampled over 5 time periods (2 days; morning and ...
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11 views

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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1answer
75 views

Hausman test after xtregar negative chi2

I'm performing a Hausman test on panel data to determine whether to choose Random Effects or Fixed Effects for my analysis with AR(1). After performing the test I get a negative $\chi^2$ statistic ...
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1answer
19 views

How do we interpret the coefficients of the random effects model?

Also, what is the difference between the interpretation of the coefficients of random and fixed effects? What I understand is this: in fixed effects, the coefficient of x shows that, what would be the ...
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0answers
8 views

Repeated measures in GLMM

I have a dataset in which individuals in some plant populations were measured over 3 consecutive years. My response variable is the reproduction of each individual. My fixed effects involve: one ...
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0answers
41 views

Estimating and comparing pre-post treatment effects in multiple pairs of treatment-vs-control groups

I have a dataset from an experiment where the goal was to study in which plant ecotype a treatment with a certain bacteria had the greatest effect. Hence the data consists of a number of control and ...
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0answers
10 views

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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2answers
97 views

How many random effects to specify in lmer?

I ran a computer-based experiment in which there were two within-subject factors, A and B. So all participants got multiple trials in each A*B cell. There was also one between subject factor, C. I'm ...
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0answers
24 views

Some doubts about using time random effect

I'm starting with lme4 and GLMM. Maybe this question can be basic for experimented researchers, but I'm still learning. I have a pooled data where every ...
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0answers
16 views

Examining the Win Probability of a Particular Play-style in a Set of Tournaments

Something I've been playing around with in my head, which I'd like some advice on. Assume you have a game with four different "play-styles" - this can be a particular strategy, different team ...
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49 views

cluster-robust standard errors are smaller than unclustered ones in fgls with cluster fixed effects

I'm currently working on some experimental data. The experimental design consists of two treatments. In each treatment, 20 subjects are randomly matched in pairs and participate to a simple game. The ...
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25 views

Longitudinal data: baseline effect versus random intercept 2

My question follows this post: Longitudinal data: baseline effect versus random intercept The topic is very interesting and I have two further questions, one very practical and another about ...
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0answers
13 views

Few-clusters bias correction for cluster robust covariance matrix in random effects model

I'm currently working on some experimental data. Subjects are randomly assigned to one of two treatments. For each treatment I ran three sessions with 20 subjects each. In each session, participants ...
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0answers
22 views

use many lms or random effects (lmer) to estimate a bunch of slopes?

I have what is probably a very simple question, but I just need someone to verify my thinking. I have a dataset that consists of a variable (var1) measured at 3 time points for about 80 people. At ...
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1answer
44 views

Does it make sense to add random coefficients to a fixed effects (fixed-intercepts) model?

If you have panel data, and you fit a model like $$ y_{it} = \alpha_i + X_{it}'\beta + \epsilon_{it} $$ then you have $E[\hat\beta] = \beta$ if you can make an argument that $E[\epsilon]=0$. This is ...
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2answers
186 views

Longitudinal data: baseline effect versus random intercept

The variable $Y$ is measured at time points $t_1$, $\ldots$, $t_9$ for each of five objects. Also available for each object is the value of $Y$ at time $t_0 = 0$ (baseline). Thus, the sample size is ...
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57 views
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45 views

Consequences of using a standard logit with heterogeneous preferences?

My understanding of the mixed logit is that it is designed to deal with situations where the population examined has heterogeneous preferences after taking into account all observed variables. The ...
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2answers
76 views

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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0answers
13 views

How to model non-linear, linear and crossed random effects in one model

I have a model with fixed and crossed random effects like this: glmer(response~1+var1+var2+var3+var3^2+(1|var4)+(1|var5)+(1|var6), family="poisson") Now, I decided that variable3 is best modelled ...
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2answers
76 views

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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2answers
186 views

Can correlated random effects “steal” the variability (and the significance) from the regression coefficient?

I have time-series count data $N_{i,j}$ (population sizes in site $i$ and year $j$) and I want to correlate year-to-year changes with the environmental conditions $x_{i,j}$. For this, I am fitting ...
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2answers
177 views

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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1answer
35 views

Correct specification of HLM in lmer

I originally learned about random effects models when taking a course on Hierarchical Linear Models, which was taught using Raudenbush and Bryk's HLM book and software, and it sort of indoctrinated me ...
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1answer
59 views

Random and Fixed effect model

Would you explain a practical situation where random effect model is more appropriate than the fixed effect model?
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52 views

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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0answers
123 views

How to interpret glmer results (variance, correlation and ICC)

I'm a beginner in statistics and I have to run multilevel logistic regressions. I am confused with the results as they differ from logistic regression with just one level. I don't know how to ...
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1answer
35 views

Equivalence of random effects via likelihood and smoothed splines

Some fake data: X = runif(1000) ff = rep(1:10,100) E = rnorm(1000) y = x+e+f f = as.factor(ff) When you fit a model like ...
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1answer
59 views

Should I exclude random effects from a model if they are not statistically significant?

Should I include random effects in a model even if they aren't statistically significant? I have a repeated measures experimental design, in which each individual experiences three different ...
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2answers
291 views

Why does SAS PROC GLIMMIX give me VERY different random slopes than glmer (lme4) for a binomial glmm

I am a user more familiar with R, and have been trying to estimate random slopes (selection coefficients) for about 35 individuals over 5 years for four habitat variables. The response variable is ...
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0answers
88 views

Multiple correlated random non-nested intercepts in R

I am trying to estimate a longitudinal model in R in which there are several random intercepts that are correlated with each other, and the data are non-nested. For example, consider a simple ...
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1answer
58 views

What is the null model for a likelihood ratio test of a within-subjects factor?

Tissue samples were taken from 4 differention locations and repeatedly measured. This was done identically for 3 animals. The research question was: Are there differences in measurement between the ...
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1answer
36 views

3 levels model with random slope in R

I'd like to estimate a 3 level model (years clustered in districts clustered in counties) on the Leyland data (Mortality in England and Wales, 1979-1992 An Introduction to Multilevel Modelling using ...
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0answers
11 views

Bootstrapping with random effects in SPSS

I'd like to use bootstrapping in my two-way ANOVA containing two fixed and one random factor. Why is the bootstrapping method not available (greyed out) for models containing a random factor? Thanks! ...
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1answer
57 views

Random Effect Model

One factor random effect model: $$y_{ij}=\mu+\tau_{i}+\epsilon_{ij}\quad i=1,2,\ldots,a; j=1,2,\ldots,n$$ where, $y_{ij}$ is the $j$th observation of $i$th treatment effect $\mu$ is the overall ...
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0answers
27 views

How many random intercepts to use?

I'm worried about the correct use of two or more random intercepts in a simple mixed logit model with a random effect and fixed effect (random intercepts model). $$Y_{ij} = a_i + B X_{ij} + e_{ij}$$ ...
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0answers
31 views

Estimated random effects correlation of 1 when fitting a random intercept model using lme

I am trying to fit the linear random effects model: $y_{ijk}=\theta_{ij}+\epsilon_{ijk},$ where $\theta_{ij}$ is a random effect. I assume the random effects and error distributions are normal. ...
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1answer
20 views

Test for differences in 2 distributions and account for a random effect

I have two probability distributions and want to say that they are statistically different. Typically, I would use a K-S test. But, my data comes from multiple individuals, which suggests I have a ...
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5answers
418 views

What is the upside of treating a factor as random in a mixed model?

I have a problem embracing the benefits of labeling a model factor as random for a few reasons. To me it appears like in almost all cases the optimal solution is to treat all of the factors as fixed. ...
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1answer
98 views

Specifying a linear mixed model in lmer with replications nested within a fully crossed design

I’m trying to specify a linear mixed model for a somewhat complicated, nested & crossed method comparison study with replicated measurements. The goal is to partition and compare variances. It’s ...
3
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0answers
45 views

When does the prediction of random effects matter?

In linear or generalized linear mixed effects models, random effects are incorporated to explain the within-unit correlation for repeated measures over time. In Bayesian modeling, conventional prior ...
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0answers
48 views

Predict random effects in a multilevel model with Empirical Bayes

In multilevel models, it is possible to predict (not estimate) the random effects by Empirical Bayes after the model parameters have been estimated. I know how to use the ...
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0answers
39 views

Random effect model result of R and JMP

I have two groups of people, namely A and B. We have the hourly mean heart rate for 45-80 hrs, length differed by individual. We are interested in the group*time effect on the heart rate. Since I have ...
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0answers
42 views

How to fit a longitudinal model with binary outcomes

I'd like to fit a longitudinal model for where multiple subjects experience binary outcomes over time. To accomplish that, I'd like to use an additive random effect for each subject and an ...
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0answers
130 views

Model diagnostics for a glmmPQL in R mixed-effects model

Several texts (both online and published books) have been reviewed prior to asking this. What diagnostics are accepted as best practise for a generalised linear mixed-effects model fitted in R using ...
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0answers
53 views

Random effects models / Integrate over the random effect

I am trying to do maximum likelihood estimation and trying to see if the problem can be formulated using a random effect model. Here is the problem description: There are $100$ pairs $(N_i, D_i)$ ...
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0answers
27 views

Fitting a Mixed Model with Random and Repeated effects in SAS

I have want to fit a linear regression with repeated measures and random effects. The data come from clinical observations. In CT images The dependent variable is the diameter of a lymph node lesion ...
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2answers
107 views

What is the right way to analyze a nested design in R?

I know that there are already a host of questions about nested designs but many of them haven't been answered or come from biological domains which I sometimes find hard to transfer to my domain. I ...