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
1answer
13 views

Time as random effect or fixed effect in glmmADMB

I have a longitudinal dataset where patients have a measurement with a date, currently coded as time from end of treatment (days). Now, I want to build a model. Roughly, a zero inflated Poisson model ...
0
votes
0answers
13 views

Mixed model random effects term is arbitrarily correlated to dependent variable, does it bias model?

I have a mixed model where a random effects term I am thinking about including is totally arbitrarily related (in a non-informative way) to the response variable. I would not include it as a predictor ...
1
vote
1answer
19 views

Is least square dummy variable model better than random effects model?

I have a panel dataset with one dependent and twelve independent variables. There are 50 individuals with data for 100 days. Theoretically, most of them should be significant. First, I checked for ...
0
votes
0answers
6 views

Long-tailed random effect posterior distribution from unbalanced design

I am using MCMCGLMM to estimate the effect of some factors on a trait. The model is constructed with $Y \backsim B*S$ to find an interaction effect of factor B ...
0
votes
0answers
9 views

code for the fixed effect (conditional) and the random effect model in SAS using panel data and a logistic+multinomial logistic model [on hold]

With respect, I am using a panel data for my thesis and I am Using SAS 9.4 for the analysis. I was wondering if I could have the chance for your advice relative to writing the codes for the model in ...
1
vote
0answers
36 views

Can a random effects intercept variable be highly but coincidentally correlated to a response variable?

Issue- I'm creating a logistic mixed model where the response variable (if a plot falls within an active bird lek area) is highly related to a term I may include as a random effects term (grazing ...
0
votes
0answers
12 views

Multilevel modelling in JAGS: Unable to resolve node

I am building a multilevel model, where the time is on the first level and countries are on the second level, in JAGS (JAGS version 3.4.0). I want to build varying-slope-varying-intercept model, like ...
0
votes
1answer
53 views

Principal component analysis with random effects?

How can I do a Principal Component Analyisis considering also the Random Effects? (*) Is there any R package able to do so? Something like PCA+lme4 or PCA+nlme. (*) I mean I want to transform my ...
0
votes
1answer
24 views

Testing for random effects: Breusch-Pagan Lagrange multiplier (LM)

So I have a panel data with two time periods. My dependent variable is an index that lies in the range of 0 to 1. I did a Breusch- Pagan test (in stata) to see whether I should use random effect or ...
1
vote
0answers
17 views

Why does glmer break when I remove a subject?

I'm working with the epilepsy data set from Applied Longitudinal Analysis by Fitzmaurice et al. (http://www.hsph.harvard.edu/fitzmaur/ala/epilepsy.txt). In this trial, 59 patients are split into a ...
0
votes
0answers
22 views

Expectation of y given u when it follows Poisson distribution

So I was reading Generalized Linear Models with Random Effects by Youngjo Lee, in chapter 6 about Hierarchical GLMs there's this example: Suppose $y|u$ is Poisson with mean $\mu = E(y|u) = ...
0
votes
0answers
14 views

Hausman Test- FE v. RE

I am estimating the parameters of my coefficients with logistic regression and I have unobserved heterogeneity. It is also a rare event. One of my primary independent variables is significant ...
0
votes
0answers
11 views

Linear Mixed Model with few and ranked subjects

I am studying linear mixed model recently, and my data have only 6 subjects and those are ranked groups of observations (Tier 1 customers > Tier 2 customers > Tier 3 > ... > Tier 6) The formula looks ...
1
vote
2answers
75 views

Industry and Year Fixed Effects

I hope someone can help me as I am stuck with this problem for quite some time. I have panel of S&P500 companies from 2010 - 2014 and I want to run a regression including industry and year fixed ...
2
votes
1answer
25 views

Using a Fixed-Effects ANOVA to help decide whether to conduct a multilevel analysis

Heck et al (2013) write that Generally, the first step in a multilevel analysis is partitioning the variance (referred to as $\sigma^2$) in an outcome variable into its within- and ...
0
votes
0answers
8 views

predict new level using lme4

i want to predict air temperature by surface temperature using lme4. my code is: plsttest=predict(mod.xm,lsttest2,allow.new.levels = T). My sample data has 19 stations and each station has 45 days. ...
0
votes
1answer
23 views

Interpreting results of a Linear Mixed effect Model

I am trying to implement a Linear Mixed-Effects Model in Matlab. I have many repeated measures of some features in a longitudinal data set of 51 people. I considered a random intercept that varies by ...
0
votes
0answers
27 views

Hausman Test Issue in R

I am doing panel data analysis with 17 variables in R using the package "plm".The panel has data for 50 firms over 60 days. The random effects model shows ...
0
votes
0answers
18 views

Nested effect in GLM

I am sorry that my question repeats many others already available here. However I have read many of them (e.g. this, this and this) as well as other documents and I am still not sure about my model. ...
0
votes
1answer
54 views

sjPlot: probabilities. How to interpret?

I am running the following model in R: model = lmer(Tau ~ ageS*days+YrsOfEds*days+sex*days+tract*days + (1|SubjectID), data=long) With this model I am ...
0
votes
0answers
18 views

Nested mixed experimental design with crossed factors for laboratory validation procedure: how to model it?

I have been asked to analyze the following laboratory experiment: 14 donors’ red blood cell samples are typed for rare antigen-asset recognition by an automated procedure (machine), which returns a ...
0
votes
0answers
7 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): ...
0
votes
0answers
7 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 ...
0
votes
0answers
18 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 ...
0
votes
0answers
19 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 ...
0
votes
0answers
35 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, ...
0
votes
0answers
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. ...
7
votes
0answers
85 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 ...
0
votes
0answers
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 ...
0
votes
0answers
32 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 ...
0
votes
0answers
34 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 ...
1
vote
1answer
21 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 ...
3
votes
0answers
47 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 ...
2
votes
0answers
49 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: ...
2
votes
1answer
22 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} ...
1
vote
0answers
19 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?
1
vote
0answers
24 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 ...
1
vote
1answer
76 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: ...
0
votes
0answers
25 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 ...
1
vote
0answers
23 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 ...
1
vote
1answer
108 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 ...
1
vote
0answers
73 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 ...
0
votes
0answers
18 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?
1
vote
0answers
35 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 ...
1
vote
0answers
58 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 ...
0
votes
0answers
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 ...
0
votes
0answers
11 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 ...
0
votes
0answers
15 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 ...
3
votes
1answer
117 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 ...
0
votes
0answers
30 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 ...