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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Random Coefficient Negative Binomial Model

I have a crash count data and i want to build a random coefficient negative binomial model in R. The dependent variable will be the crash counts and covariates will be Lane width, AADT, shoulder width ...
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29 views

Assessing the need for random effects terms

During the model selection phase for mixed models, there are typically several possibilities to choose from; in fact, the number of possibilities is increasing in the number of covariates used. How ...
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26 views

interpreting a 3 way interaction between categorical variables with mixed model with random effect

I have ran a glmer analysis with R. This question have been comment regarding three way interaction for ANOVA models. this is clearly not the same thing as a general linear mixed model first because ...
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18 views

xtreg, re in STATA, which R2 to report? [duplicate]

After estimating the data using xtreg, re, I notice there're 3 different measures of R-squared, within, between, and overall R-2, so my question is, can I just report the overall R2 in this case since ...
3
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1answer
35 views

Estimates of random effects in binomial model (lme4)

I'm simulating Bernoulli trials with a random $\text{logit}\, \theta \sim {\cal N}(\text{logit}\, \theta_0, 1^2)$ between groups and then I fit the corresponding model with the ...
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9 views

Group mean centering predictors for crossed random effects

I'm fitting a mixed-effects model, in which I wish to test the effect of $X$ on $Y$, with crossed random intercepts and slopes for each subjects $S%$, and for each level of an additional grouping ...
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1answer
15 views

Using subject-specific random intercept to account for repeated measures over time

I have an epidemiologic study on subjects with yearly repeated measures on a count variable as an outcome and various yearly measured predictors. The study population changes every year somewhat, so ...
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0answers
26 views

Fixed vs. Random Effects models for binary response: Understanding “glmer” output “Correlation of Fixed Effects”

There seems to be very little explanation about the summary output obtained from the glmer package in R. To elaborate, I will start with my understanding of the Fixed Effects (FE) and Random Effects ...
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16 views
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43 views

Pooled OLS, fixed, random or mixed effects?

I am analysing a simple balanced panel data with the following variables: ...
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34 views

How can i calculate an effect size (cohen's d) from a linear random effects model (beta)

I am trying to figure out how to calculate a Cohen's d statistic for a linear random effects model. I did not do the analysis myself, I have read it in a journal article so i'm left to figure it out ...
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1answer
65 views

lme4: random effects

I have a simulated data set of 4 repeated measurements (measure) for 5 subjects (subj), 20 trials (trl) each. I am trying to fit a model with random slopes for age category with subject and trials ...
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0answers
23 views

Modelling time invariant variable as a random effect

I have a data set that consist of one-year claim information of individuals. Individuals can have zero claim, one claim and more than one claim in a year. Generally they have more than one claim and ...
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2answers
67 views

Random intercept turns insignificant - Interpretation

I am running a intercept model with intercepts varying across people in R. My independent variables are all numeric variables. My question is a general one: I saw already that it can happen that my ...
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0answers
45 views

Study on both hands of patients, mixed effects?

We want to test the correlation of a certain surgical procedure on the hand (carpal tunnel) and the development of trigger digit. We have both hands of the patients, some hands underwent surgery, ...
2
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1answer
42 views

Panel data estimation for country-fixed, time-varying share of y

I want to estimate the following equation using a panel data set with countries $i$ and years $t$, preferably in R: $$ y_{it} = \beta_1 \cdot x_{1,it} + \beta_2 ...
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0answers
31 views

Box cox for mixed models in R

Consider a mixed model generated using the lme function in R. How can I consider the Box-cox transformations of this model in R? I have seen similar questions being asked before but they did not give ...
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0answers
9 views

Panel model Estimation - 5 and 10 years Break [migrated]

I am trying to estimate the relationship between inequality and growth in a panel data of 22 countries for the period from 1985 to 2010, using fixed-effect and random effect. I want to use 5-year and ...
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1answer
30 views

Autocorrelation *across* random effects in nlme:lme?

I have response data measured at the site and month level. I wish to fit a year trend to the data and month to remove the seasonal trend. However, to avoid pseudoreplication, I have fitted year also ...
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0answers
37 views

Mixed effect partial proportional odds ordinal regression in R

I want to ask if there is a package in R that can fit and more importantly predict a clustered (random effect) ordinal regression with non-proportional ...
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1answer
48 views

How to specify a random slope only in glmer

I'm trying to build a glmer model and want a random effect of individual ID but only need the random slope. I've been searching the internet for awhile but I'm still not 100% sure how to specify the ...
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1answer
80 views

Hausman test for panel data

I am performing a Hausman test to decide whether to use fixed effects or random effects model. The results I get are as follows: ...
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0answers
33 views

Random effects model with time varying predictors and invariant outcome

I'm considering a random coefficient model with a time-invariant outcome and both time-varying and time invariant predictors. I have introductory knowledge on the topic. My outcome is a performance ...
2
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0answers
42 views

Heteroscedasticity test for random effects model in Stata

I have a panel data and according to Hausman, I have to use a random effects model. I know that in Stata I can use a modified Wald test, but only with a fixed effects model. I want to know a test for ...
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0answers
19 views

Fitting a multilevel AR1 in R

I have some short grouped time series data. I would like to fit a dynamic multilevel regression model in R, with random coefficients for the mean and first order auto-correlation in each group, and ...
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0answers
19 views

high standard error in random intercept model

I am fitting multilevel logistic regression to left without being seen (LWBS) by providers as dependent variable for each visit to 6 hospitals (random intercept of each hospitals). The total number ...
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1answer
40 views

Comparison between a multilevel and an unpooled model

Suppose we have fitted two models: a multilevel model and an unpooled model: m1=lmer(y~x+(1|group)) m2=lm(y~x+factor(group)-1) How can I understand which ...
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1answer
101 views

Fixed effects or Random effects model?

I am trying to understand the difference between fixed and random effects modelling. The panel data I have is in the form of basic longitudinal panel time series. I know that I can use the Hauseman ...
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1answer
26 views

Mixed-effects model without the random effect in the design?

Is it appropriate to create a mixed-effects model (for example, using SAS Proc Mixed) that specifies a random effect but does not include the random effect in the model itself? I ask because it seems ...
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0answers
69 views

Testing and correctly interpreting the significance of nested random effects

I'm building a series of relatively simple random effects models where we repeatedly measure a water quality variable, say conductivity (cond), in different watersheds (ws) and streams (st). Here, ...
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0answers
31 views

What is a case where including random correlation parameters decreases the error on the fixed effects?

I'm wondering about the effect of true correlations among random effects on the standard error of my fixed effects in lme4::lmer models in R. My assumption is ...
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1answer
72 views

Random effects for within subjects study in R

I have data which consists of subjective ratings. The ratings are done by different judges with each subject being judged under different experimental conditions. Each subject undergoes each ...
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0answers
28 views

Significant cross-level interaction despite lack of variance in level-1 slopes

I have a logistic HLM model with one level-1 predictor and without level-2 predictors. Random variance components are significant for intercepts, but far from significant (p>.5) for slopes. In my ...
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1answer
22 views

What is the difference between a random-effects regression and the negative binominal regression

My question is related to the topic if my data is overdispersed is random-effects poisson regression a suitable option? Thanks for any hint!
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1answer
25 views

Intercept in random effects mixed model no longer significant

When I add a categorical fixed effect to my mixed model (with one random effect and three continuous fixed effects) the intercept is no longer statistically significant. Does this mean that the newly ...
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0answers
12 views

How to interpret change in intercept when a random effect is added to a mixed model

I have a fixed effect model, constructed in SAS using Proc Mixed as: proc mixed data= etc...; model ca = p t s /solution; run; which yields the following ...
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1answer
27 views

Analysis of clustered data

I have records of $multiple$ visits from many different patients in several different clinics (i.e. visits nested within patients nested within clinic) and plan to perform an analysis that takes into ...
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1answer
30 views

Regression formula for negative binomial with random effects

I am doing a negative binomial regression with random effects. I have Panel data on 23 countries ($i$) across 28 years ($t$), with one dependent, one independent and three control variables. I do ...
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0answers
6 views

For forest research to see the interaction of environmental variables when to use GLMM

I am testing different environmental variables (altitude,slope,aspect,trampling,cut stumps) with tree density, seedlings and saplings, basal area and also with soil parameters like soil carbon and C:N ...
2
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1answer
107 views

R-Metafor: Meta-analysis and mixed effect model

This is an example of my dataset ...
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0answers
65 views

Comparison of crossed random effects (mixed models): lmer vs. MCMCglmm

I read that lmer can handle independent (often labeled as crossed) random effects in mixed models. It seems to be possible with MCMCglmm as long as groups for the random effects are uniquely labeled. ...
2
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1answer
80 views

Interpret effect of adding random effects to ordinal regression (R - ordinal package - clmm)

I know there are already lots of questions around this topic (especially this one and this one) but I haven't really seen anything that directly helps me (It will be obvious I'm not a great ...
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1answer
29 views

Mixed model (Multilevel) with two INDEPENDENT Random Effects [lmer]

I like to estimate a mixed model with two Random Effects, that are independent of each other and among themselves. I use Panel data with a nested structure (counties $j$ nested within regions $i$). ...
0
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0answers
35 views

How does NLMIXED estimate random effect for a 2-part (hurdle) model?

I am not a big SAS user, so this may be a naive question. I am using the NLMIXED procedure to estimate a random effects 2-part (hurdle) model, similar to as described here but with random effects for ...
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1answer
31 views

What software could fit an alternative specific random intercept multinomial logit model?

I have a dataset of around 30,000 people where each chooses one of 4 items: A, B, C or D. People are nested within the 600 areas. I want to fit a multinomial logit model, where random intercepts are ...
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1answer
107 views

lme4: Why is AIC no longer displayed when using REML [duplicate]

I have a simple question, understanding the basic usage of the lme4 package. I am following the tutorial by Bodo Winter ...
3
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1answer
85 views

Can we model non random factors as random in a multilevel/hierarchical design?

The distinction between strictly random variables (which ought to be modeled as such) and non random variables which some argue could be modeled as random if it is a hierarchical/multilevel model, is ...
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0answers
16 views

choosing the best structure of the random effects in a GLMM [duplicate]

I am trying to choose the best random effect structure in a GLMM, before starting with the fixed terms. To do that I include all the fixed effect and their interactions (beyond optimal model) and ...
0
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1answer
95 views

Predicting with random effects in mgcv gam

I am interested in modeling total fish catch using gam in mgcv to model simple random effects for individual vessels (that make repeated trips over time in the fishery). I have 98 subjects, so I ...
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0answers
10 views

P-values for random effects when using REML [duplicate]

I'm using JMP to fit a model for an unbalanced split-plot design. Because it's unbalanced, I'm using REML rather than EMS. However, I would like to get test statistics/p-values for some of the random ...