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."

learn more… | top users | synonyms

0
votes
0answers
2 views

confidence interval for I square

in my metanalysis I got a negative I square value (Q=2.040; df=4). I will report it as zero. Do I need to report confidence interval for I square? Is it O.K to use fixed effect model for above ...
0
votes
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 ...
1
vote
0answers
50 views
1
vote
0answers
23 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
votes
1answer
31 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 ...
0
votes
1answer
13 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
votes
0answers
12 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 ...
1
vote
1answer
15 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 ...
1
vote
1answer
51 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
votes
1answer
71 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 ...
0
votes
0answers
15 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
votes
1answer
28 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 ...
0
votes
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 ...
1
vote
0answers
23 views

lmer, effect of a ramdom factor in a covariate [closed]

hi I am doing a General Linear Mixed Model in R, lme4 package. I want to test the effect of a random effect on a covariate, as well as on the response variable. I thin that the command would be the ...
2
votes
1answer
29 views

Calculating heterogeneity (Cochrane Q and I^2 statistics) in random effect model

I want to ask whether I can calculate the Cochrane Q statistics and I^2 statistics in random effect model. I collected some data from different areas and want to compare the prevalence of allergic ...
2
votes
0answers
26 views

Fast Estimation of Random Effects with Measurement Error

Let $Y_{i}$~$Normal( Z_{i} , \sigma_{i}^{2})$ and $Z_{i}$~$Normal(0,\tau^{2})$, where $Y_{i}$ is a measurement, $\sigma_{i}$ is known measurement error, $Z_{i}$ is the unknown latent variable I want ...
2
votes
2answers
95 views

Can I include covariate as random effect in glmer?

I have a question regarding covariates in a GLMM. My model comprises a condition variable and a covariate. Crucially, my binomially distributed dependent variable can be interpreted only in dependency ...
2
votes
1answer
106 views

lmer and random effects

Here is somewhat simplified structure of the data I have, since fixed effects are quite straight forward, however, random effects are giving me a headache (like I said something new :) ): ...
0
votes
1answer
49 views

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 ...
1
vote
0answers
24 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 ...
2
votes
1answer
89 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 ...
2
votes
1answer
60 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 ...
3
votes
1answer
50 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 ...
0
votes
0answers
33 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 ...
1
vote
1answer
52 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): ...
0
votes
0answers
52 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, ...
0
votes
0answers
26 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 ...
2
votes
2answers
115 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 ...
7
votes
1answer
440 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 ...
1
vote
2answers
64 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 ...
0
votes
1answer
23 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 ...
0
votes
1answer
40 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 ...
0
votes
0answers
33 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 ...
1
vote
0answers
36 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 ...
0
votes
0answers
9 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 ...
1
vote
0answers
18 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 ...
1
vote
1answer
170 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 ...
0
votes
1answer
31 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 ...
0
votes
0answers
13 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 ...
1
vote
0answers
52 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 ...
0
votes
0answers
23 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 ...
4
votes
2answers
120 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 ...
0
votes
0answers
30 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 ...
0
votes
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 ...
1
vote
0answers
68 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 ...
1
vote
0answers
26 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 ...
0
votes
0answers
20 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 ...
2
votes
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 ...
0
votes
1answer
56 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 ...
5
votes
2answers
200 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 ...