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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6 views

glmer returning more random effects than specified?

I am estimating a model of the type (logistic regression with random slopes and random intercepts clustered by the variable ID): ...
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29 views

Basic questions about a econometric analysis of paneldata [on hold]

For a research project in economics, I want to study the several determinants that affect the decision to enter the private rental housing market in the United States. There is a special focus on ...
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3 views

Using additional data to improve random effect estimates in multilevel model

I've got a set of measurements (axonal transport measurements for individual axons nested within animals in two treatment conditions: drug vs control) which I currently analyse using a random ...
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12 views

Queries regarding 'logistftest' function of logistf package for model comparison

I am a user of r package ‘logistf’, I have 2 queries regarding the ‘logistftest’ function when doing model comparisons, I would appreciate if anyone can give helps. I am comparing two models (one ...
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18 views

'random intercept only', 'random slope only' and 'random intercept and slope' models

I have a terminological question about the use of the terms random intercept models, random slope models and random intercept and slope models. Through my readings, I find that most authors use the ...
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30 views

Fixed Effect, Random Effect vs. Mixed effect and distribution assumption

From my econometrics class, we have learned that the difference between fixed effect and random effect is the assumption on the unobserved heterogeneity of the group. If one were to use random effect, ...
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7 views

Linear mixed effects models: random intercept has a VPC of 0.56% yet significantly increases model fit?

So basically what I said in the title. The model is based on data from an experiment where participants look at faces and makes judgement on them. I have a sample of 40 faces and 3500 participants. ...
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77 views

Specifying prior for effect size in meta-analysis

My question concerns priors on effect sizes, in my project the measure is Cohen's D. Through reading the literature, it seems vague priors are often used, such as in the well-know eight schools ...
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6 views

GLMER casemix without random slopes

Imagine we're evaluating a large group of cars (our random effect) for reliability, by looking at data on various driving conditions (our fixed effects). We come up with a GLMER model that ...
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0answers
23 views

Changepoint mixed model R2jags

Can anyone suggest a way to code a changepoint model in JAGS (I'm using JAGS within R using R2jags) for the variance parameter of a random intercept effect? I am using the data set sleepstudy from ...
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30 views

Assistance with methodological procedures for (panel data) [investment/GDP]

I am attempting to see the relationship between FDI and Economic growth. So my two variables are FDI and GDP growth (I am also not opposed to using GDP per capita). My data is panel, as I am using 59 ...
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1answer
20 views

Hausman test for models with different regressors

I want to know if I can run a Hausman test for Random Effects and First-Difference models with different regressors? For Example: MODre = X + Y + Z MODfd = X + Y + C Where X, Y, Z, C are some ...
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43 views

Controlling for individual in nlme when most individuals only measured once

I am trying to model growth using nlme for a number of individuals over four time periods. My question is, did growth differ over time? Some individuals were measured twice or more, perhaps as a young ...
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43 views

Interpreting the variance of random effects in Mixed Linear Models

When fitting the following simple model, using the 'lme4' R package and including a fixed and random slope term, I get: ...
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1answer
21 views

Random effects model: Observations from the same level have covariance $\sigma^2$?

I'm trying to understand what the following means (or how it's displayed): For a one way random effects model: $$Y_{ij}=\mu+\alpha_i+\epsilon_{ij}$$ $\alpha_i \sim N(0,\sigma_A^2)$, $\epsilon_{ij} ...
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0answers
18 views

Find Maximum Likelihood function for the parameters of a random effects model

Could someone explain me how to get the maximum likelihood function for the parameters of a random effects model? Besides what the assumptions of this model are?
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22 views

How to estimate the parameters of the following log-likelihood function?

I would like to estimate the parameters based on the famous Merton model used probability of default modelling: Suppose firms' logarithmic returns are following the standard normal distribution and ...
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1answer
54 views

R lmerTest step() function returns significant random effect without equivalent significant fixed effect

I do have a 2 level data set with 3 observations nested in one person. I am fitting a mixed model including 71 predictors and 28 random slopes in the following manner: ...
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20 views

In random effects models, why do we integrate out the random effect?

I think I've seen some papers that don't integrate out random effects when computing the likelihood function and have also seen some papers where random effects are integrated out. Does this have ...
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19 views

Change a random effects model into a fixed effects model in *BUGS

I want to manipulate this RE model into a FE model for sensitivity analysis. The model is described here: Baseline natural history model ...
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1answer
77 views

Random effect model: residual variance interpretation

I have ran a random effect regression to work with a panel data on Stata: xtreg lc ly lpl lpm ,re I got this output but I have some troubles in interpreting its ...
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0answers
53 views

Beta regression with random effects in R: different results in GAMLSS vs. glmmADMB

I am trying to fit a beta regression model to some repeated-measures data. I fit the model both with the function glmmadmb() in the ...
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0answers
17 views

Difference between fixed and individual effects

I have recently started studying about panel data and I am confused about the term "Individual effects". Is it correct to think of fixed and random effects as subsets of Individual effects?
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23 views

Pooled OLS vs Random Effects difference in coefficients

My lecture slides say that a reason for why coefficients for some of the Pooled OLS model and Random Effects are different in a specific example could be because unobserved time-invariant factors are ...
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40 views

Stata: plotting the random+fixed effects of slopes after multilevel models are fit with `xtmixed`

I am fitting a linear multilevel model of the following form: Y = b0 * X0 + b1 * X1 + b2 * X2 + b3 * X3 + u b0 = e0 b3 = e3 The units in the first level are ...
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27 views

lmer = Fitting mixed model from complex to simple (backward)

I've been trying to run some analyses using mixed effect models in R, but the more I read about it, the more the questions I have. I'm sorry if some of those might seem obvious and rather silly, but ...
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10 views

How to model random effects with signal detection data

I'd like to analyze data from an signal-detection experiment and I got confused with the different possibilities to model random effects. I'm using glmer with data in long format. Participants saw ...
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14 views

How to write a model with interaction and random factor in mixed models?

I am looking for help to formulate my model. I am working with longitudinal data, where my response variable (Y) is the number of eggs. My fixed effects are (X1) the standardized initiation day of a ...
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1answer
101 views

Is it reasonable to include a random slope term in an lmer model without the corresponding fixed effect?

I have an experiment in which I presented multiple stimuli to participants and wanted to control for the order in which the stimuli for shown. I am curious if it's possible to only account for order ...
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0answers
26 views

Nested Manova with random nested factor won't run!

I'm running a multivariate analysis on a nested design with a random factor (Site) nested within a fixed factor (Region), with five dependent morphological variables. I have two sites within each of ...
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1answer
22 views

Binary variable shows twice in random effects when random intercept excluded [R, lme4]

When I use lmer of lme4 to fit a random one-variable slope model with random intercept excluded, both levels of the one-variable ...
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0answers
13 views

BLUP results: confusing for me

We have disease infestation data of a germplasm set (200 genotypes), which were replicated thrice (3 blocks: Complete set of genotypes were randomized in each block) in single location for three ...
4
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1answer
122 views

Two methods of adding random effects to a GAM give very different results. Why is this and which one should be used?

A particular section of the mgcv documentation gives multiple methods of incorporating random effects into a generalized additive model. Two methods are 1) to add a smooth term in the class labels ...
3
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23 views

Likelihood and DF for random effects models in gam (mgcv) vs. lmer?

I am aware that there is a duality between random effects and smooth curve estimation. At this link, Simon Wood describes how to specify random effects using mgcv. Of particular note is the following ...
3
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27 views

Test whether random slopes are significantly different from 0 for individual subjects

I am working (in R 3.2.3 using lme4 for doing mixed effect modeling) with vowel data from many different subjects. The question I'm interested is whether certain ...
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1answer
30 views

How does the random coefficient model take care of autocorrelation?

I’m working with time series data (samplings been done for about every two weeks in 1,5 year) from several subjects where they have measured different variables changing in time. The main goal is to ...
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0answers
8 views

How to handle with a very small sample and unbalanced dataset? Is GLMM a good option?

I'm having some troubles in analyzing and make the most of my dataset. My hypothesis: pharmacological condition maximizes utility of the decision regardless of the context (by providing the necessary ...
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0answers
13 views

How to handle with unbalanced and small sample dataset? Is GLMM a good option?

I'm having troubles in analyzing and make the most of my dataset. My hypothesis: pharmacological condition maximizes utility of the decision regardless of the context (by providing the necessary ...
2
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1answer
36 views

What are the criteria to be a random factor in a multilevel model?

In multilevel data, observations are correlated in different levels and when we model the data we consider these levels as random variables. Suppose we have only 6 regions in my data and the ...
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0answers
68 views

Using lme4 correctly for a nested mixed model

I'm trying to create a nested model in lme4 and would like feedback on whether I've understood its use correctly. Every individual (n = 144) had two proteins measured after 0, 12 and 72 hours. At ...
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0answers
24 views

Choose between models FE vs RE vs Pooled OLS

I have quarterly data for 3 countries for a period of 10 years. Number of observations = 123. I have the following two questions: I would like to know what tests should I perform to choose between ...
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0answers
15 views

Analyzing frequency of medical procedures by hospital and patient characteristics

I'm working with state-level HCUP data. This mostly includes patient characteristics (age, gender, race, etc.), diagnoses and procedures, as well as info on the hospital including bed size, ...
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0answers
16 views

Random effects structure for within-subject / repeated measures design in glmer

This post is somewhat related to this one, however with another focus. I have repeated measures data of plant species richness with the following variables: ...
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1answer
41 views

Reporting of Heterogeneity in Metaanalysis

The background is a forest plot of a meta-analysis which is reported to have been calculated with a "random effects model". The pooled effect is reported as a standardized mean difference (SMD). ...
0
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1answer
35 views

Random effects, fixed effects, or perhaps nested fixed effects?

Simple question (I hope). I have the following experimental design: Two groups: A, B (let's say they represent the two sexes), where I randomly sampled 4 subjects from each group, and measured blood ...
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61 views

Unequal variance and crossed random effects in linear mixed effects model

I'm analyzing an experiment that has 50 subjects and 50 items. Each item can occur in two possible conditions. Every experimental subject produces a (continuous) response to every item, but only once ...
3
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0answers
57 views

Shall I use a random effect or not?

I need to see if in the case I am going to present it is worth to use a random effect or not. I carried out some bird counts from 9 elevated lookouts in an island. Just to orient you, these lookouts ...
3
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1answer
47 views

Under what conditions are these mixed model formulations equivalent?

I see models for "mixed effects" (i.e., models with fixed as well as random factors) specified in the literature in two ways, and I'd like to understand the conditions under which they are ...
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0answers
26 views

What is “nested” when fitting models in r?

I'd like to know what does "nested" mean when fitting a model using R. Here is a tutorial1 of the difference between "nested" and "cross". Here is another tutorial2 teaching the two way repeated ...
2
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1answer
139 views

Repeated measures - random effects for logistic regression in R?

Study design 504 individuals were all sampled 2 times. Once before and once after a celebration. The goal is to investigate if this event (Celebration) as well as working with animals (sheepdog) ...