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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Is this logit model a multilevel model, and what is the correct way to model it?

I am analyzing a sample of about 6000 actions carried out by about 500 multinational companies in about 80 countries during a 6 year period. Actions are carried out randomly, and are not longitudinal ...
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20 views

Zero-intercept poisson regression model predicts better than a model with an intercept?

I have read some blogs/articles saying that intercept should not be suppressed. Recently, I used glmm.admb to model a ZIP (Zero-inflated Poisson) model which is giving better results without an ...
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1answer
41 views

Recommendation: Fast algorithm for logistic random effects?

What is the fastest algorithm for fitting a simple logistic 'random effects' type model, with only one level of categorical predictors? Another way of putting it might be a logistic regression with ...
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24 views

2 factor factorial model with random factors

Using lme4, how does one define a full 2-factor factorial model with both factors (and their interaction) being random? Specifically I am trying to recreate the ...
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1answer
29 views

Hausman test FE vs. RE in case that FE is not consistent

I have a twin panel data and want to estimate simple wage equation (interest in return to education). I use fixed effects(first differencing) to account for family background.(Based on: Ashenfelter ...
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0answers
23 views

Is my nested random-effect model non-hierarchical?

I have a problem with model structure because of the way factors are nested in a potentially non-hierarchical way. I'm not sure if I fully understand the issue but I can't find a way to specify the ...
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1answer
42 views

Random-effects probit model

I am currently using a mixed binomial model with the following specification in a paper I recently submitted (using lme4): ...
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40 views

In R package lme4, how do you force the random slopes and intercepts to be uncorrelated for an interaction term?

I have a mixed model, fit using lmer in R, that has three interaction terms (X1:X1, X1:X3, ...
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0answers
16 views

GLIMMIX convergence problems with ordinal model

I am analyzing a longitudinal data set about hearing loss which has 226 subjects with repeated measures each one (with a maximum of 12 observations per subject) over a follow up time of 22.2 years. I ...
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2answers
73 views

Time-invariant variables not being removed in Fixed Effects model. And feasibility of addional time dummies in Fixed Effect/Random modelling

I am working with severely unbalanced Panel Data of a nations Fisheries where I have individual data from all deliveries made by every single vessel. Thus far I have reshaped the data so that every ...
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1answer
428 views

How to use the Hausman test for gender discrimination?

I am trying to estimate the gender wage gap for male and female office workers in a large Swedish company to test whether there is gender discrimination. The Hausman test rejects the null that the ...
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2answers
47 views

Panel data methods

My dataset is following: firms=1000, time period=10 years, countries=20, industries=15. I declare in STATA: xtset firmid year I want to control for the ...
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1answer
15 views

Wrong error term for test of random factor in two-way mixed-effects ANOVA in SPSS

In SPSS, when I run a two-way mixed-effects ANOVA (with factor A fixed and factor B random), the between subjects effects table reported by SPSS uses mean square for the interaction (MS_AB) as the ...
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1answer
35 views

Are level 1 and level 2 residuals in a mixed effects model always normally distributed?

Take this mixed effects model: $y_{ij} = \beta_0 + \beta_1X_{ij} + \mu_{j} + \epsilon_{ij}$ The level 2 residuals are $\mu_{j}$ and the level 1 residuals are $\epsilon_{ij}$. As I understand the ...
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0answers
32 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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0answers
21 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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7 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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0answers
16 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
111 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
26 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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11 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
46 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
15 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
107 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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26 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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56 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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18 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
25 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
51 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
189 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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81 views
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50 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
112 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
96 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 ...
5
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2answers
187 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 ...
2
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2answers
283 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
44 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
63 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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0answers
55 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 ...
2
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0answers
157 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 ...
3
votes
1answer
39 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 ...
5
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1answer
66 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 ...
6
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2answers
341 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 ...
2
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0answers
95 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 ...
3
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1answer
65 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
13 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! ...