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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24 views

How many random intercepts to use?

I'm worried about the correct use of two or more random intercepts in a simple mixed logit model with a random effect and fixed effect (random intercepts model). $$Y_{ij} = a_i + B X_{ij} + e_{ij}$$ ...
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0answers
11 views

Estimated random effects correlation of 1 when fitting a random intercept model using lme

I am trying to fit the linear random effects model: $y_{ijk}=\theta_{ij}+\epsilon_{ijk},$ where $\theta_{ij}$ is a random effect. I assume the random effects and error distributions are normal. ...
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1answer
19 views

Test for differences in 2 distributions and account for a random effect

I have two probability distributions and want to say that they are statistically different. Typically, I would use a K-S test. But, my data comes from multiple individuals, which suggests I have a ...
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4answers
172 views
+50

What is the upside of treating a factor as random in a mixed model?

I have a problem embracing the benefits of labeling a model factor as random for a few reasons. To me it appears like in almost all cases the optimal solution is to treat all of the factors as fixed. ...
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0answers
39 views

Specifying a linear mixed model in lmer with replications nested within a fully crossed design

I’m trying to specify a linear mixed model for a somewhat complicated, nested & crossed method comparison study with replicated measurements. The goal is to partition and compare variances. It’s ...
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0answers
37 views

When does the prediction of random effects matter?

In linear or generalized linear mixed effects models, random effects are incorporated to explain the within-unit correlation for repeated measures over time. In Bayesian modeling, conventional prior ...
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0answers
13 views

Predict random effects in a multilevel model with Empirical Bayes

In multilevel models, it is possible to predict (not estimate) the random effects by Empirical Bayes after the model parameters have been estimated. I know how to use the ...
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0answers
23 views

Random effect model result of R and JMP

I have two groups of people, namely A and B. We have the hourly mean heart rate for 45-80 hrs, length differed by individual. We are interested in the group*time effect on the heart rate. Since I have ...
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0answers
39 views

How to fit a longitudinal model with binary outcomes

I'd like to fit a longitudinal model for where multiple subjects experience binary outcomes over time. To accomplish that, I'd like to use an additive random effect for each subject and an ...
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0answers
37 views

Model diagnostics for a glmmPQL in R mixed-effects model

Several texts (both online and published books) have been reviewed prior to asking this. What diagnostics are accepted as best practise for a generalised linear mixed-effects model fitted in R using ...
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0answers
26 views

Random effects models / Integrate over the random effect

I am trying to do maximum likelihood estimation and trying to see if the problem can be formulated using a random effect model. Here is the problem description: There are $100$ pairs $(N_i, D_i)$ ...
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0answers
13 views

Fitting a Mixed Model with Random and Repeated effects in SAS

I have want to fit a linear regression with repeated measures and random effects. The data come from clinical observations. In CT images The dependent variable is the diameter of a lymph node lesion ...
2
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2answers
71 views

What is the right way to analyze a nested design in R?

I know that there are already a host of questions about nested designs but many of them haven't been answered or come from biological domains which I sometimes find hard to transfer to my domain. I ...
2
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1answer
91 views

Difference between random effect and random intercept model

I am looking at clustered data and because I was trained in economics I tend to look at fixed effects and random effects as solutions. An alternative would clearly be multi-level modelling. However, ...
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0answers
18 views

allowing for different variances between groups versus allowing for random slopes in lme

I have a data set that looks like this: Genotype Condition Trait A 1 0.0007 B 1 0.005 A 2 0.0003 B 2 ...
2
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0answers
80 views

Random Effects Tobit Model

I'm studying the effect of various criminal case and court district characteristics on sentence lengths. I was planning on running xttobit in Stata because I have individual defendants/cases within ...
2
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1answer
50 views

Are best linear unbiased predictions (BLUPs) a good indicator of the mean value for that random effect member?

I have data on prices of houses in different districts, and would like to determine how expensive different districts are when it comes to buying a house. However, houses vary with respect to ...
2
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1answer
120 views

Is it possible to use the Breusch-Pagan Lagrange multiplier test (xttest0) in Stata for unbalanced data?

Is it possible to use xttest0 in Stata with unbalanced panel data? I want to test whether the I should use pooled OLS or random effects estimation. What does this test actually do?
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1answer
47 views

How to perform a meta regression with a random effect model?Which model should I use?How to start? (beginner)

I have to perform a meta-regression, using mixed or random effects model, but I don't have any software (except Matlab) and I'm new on this topic (having a relativelly poor statistics background). ...
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0answers
40 views

Within-subject and between-subject fixed effects in mixed model

I've been trying to analyze some data using mixed models but I have some troubles to understand how should I include both within-subject and between-subject fixed effects in such models. Let's ...
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0answers
33 views

How to estimate a dynamic Tobit model

I have data which correspond to a corner solution. The Tobit-model seems to be adequate for this data. However, I also wants to control for a baseline variable (t-1) and unobserved heterogenity. This ...
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0answers
43 views

Recovery of Standard Errors of Random Effects in Lmer

I'm analysing data with a nested structure with the lmer-function of the Lme4 package in R. I'm interested in the estimation of the confidence intervals of the random effects (is the score of class1 ...
0
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1answer
68 views

Testing significance of a random effect glmmADMB model

Below is the output from a model of novel object test scores fit with the nbinom1 (quasi-Poisson) option in glmmADMB. I used ...
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2answers
180 views

How can I include random effects into a randomForest

I'm not even sure that the question makes much sense, but I think I saw a couple of titles of papers where they proposed random forest with random effects. Is this possible in R?
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0answers
21 views

Do I need by-item effects in my lmer model, with gain scores as DV?

I'm attempting to fit a relatively straightforward linear model in R, but am in doubt as to whether by-item effects should be included in the model. Any input would be most appreciated! Study design: ...
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1answer
46 views

Residual specification for xtmixed

Suppose a random intercept model is to be fitted, like: $$y_{ij}=\beta_0 + \beta_1x_{1ij} + \beta_2x_{2ij} + \beta_3x_{3ij}+ u_{0j} + \epsilon_{ij}$$ where $x_{1ij}$ and $x_{2ij}$ are continuous ...
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0answers
25 views

Can time-invariant variables cause autocorrelation?

I am running a pooled OLS regression and Random effects regression. I have tested for autocorrelation for both methods. In the pooled OLS model I find serial correlation but for the RE model I find ...
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0answers
42 views

Intuitive explanation of “integrate out random effect”

We are trying to figure out an intuitive reasoning behind integrate out the unobserved random effect. The specific formula is: $f\big(y_i|x_i;\beta, ...
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0answers
24 views

Testing the random slope with correlated random effects

I have a mixed/random effects model $$\mathbf{y}_i=\mathbf{X}_i\boldsymbol\beta+\mathbf{Z}_i\mathbf b_i+\boldsymbol\epsilon_i,$$ where random effects $\mathbf b_i$ has variance-covariance matrix ...
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1answer
43 views

Hausman's test for all $\beta$s – comparing FE vs RE models

I fit several two level models in SAS using PROC MIXED: an empty model with multilevel structure (null), a model with a level 2 covariate (partial model), and a ...
0
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1answer
27 views

How does including a random effect, change the parameter estimate for a group level covariate?

I'll try and explain where I am getting stuck. I wish to model delay to treatment. I observe patients nested in hospitals. I have a mixture of patient and hospital level covariates. I suspect that ...
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0answers
13 views

What do I do if my sample for cross sections is not random in a panel regression model?

I am trying to implement a panel regression model, but there is one issue: both fixed effects estimation (FE) and random effects estimation (RE) require that the cross section sample be random. For my ...
2
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1answer
48 views

how to fit a michaelis-menten function with a random effect using the nlme package in R?

I am working on fitting a model using the nlme package in R. y is a saturating function of x, similar in form to a ...
0
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0answers
70 views

How to test for heteroskedasticity in Random Effects model?

I know how to test for heteroskedasticity using pooled OLS? But how can I do it after I have run a Random Effects model? If I find heteroskedasticity in pooled ols does it mean it is also present in ...
2
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0answers
46 views

How to detect outliers with longitudinal data?

I am running a pooled OLS and Random Effects (RE) model and I would like to test for whether there are any outliers. I know how to do this for OLS, but I just dont know how to do it for Random ...
2
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0answers
30 views

How would you model this random effects structure?

I have a sort of weird and complicated model design, and I'd like to get your opinion on how best to model the error structure. I have 100 sites, with each site falling into 1 of 4 different forest ...
1
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0answers
21 views

Should I control for random effects of participant in an individual differences design?

I'm trying to analyse a survey study in which I'm interested in the way that individual differences between my participants influence how they respond to my stimuli. The stimuli are pieces of writing ...
1
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0answers
18 views

Growth mixture models, “structural parameters”, and using a parameter with variance fixed at zero as mediator in regression model?

I am examining a paper that uses growth mixture modeling. They have estimated intercept, linear slope, and quadratic growth parameters. They have also estimated random effects for the intercept and ...
1
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0answers
54 views

Random effects and repeated measures: what would you choose as random effect?

I have a question regarding the use of random effects in order to account for a violation of the assumption independent samples. I have this discussion with my supervisor and we disagree about this ...
1
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1answer
72 views

When to use time dummies in multiple regression

When is it appropriate to use time dummies in multiple regression analysis? I am using pooled ols, random effects model, and fixed effects model. I have a period of 3 years. I don´t know whether it ...
0
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0answers
45 views

Estimated SD equal to 0 (lmer)

I'm trying to fit a mixed model of my data, but I'm getting the estimated between-subject standard deviation equal to zero. I need to estimate the between-subject standard deviation and within-subject ...
0
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0answers
126 views

R: glmulti for mixed models returns several best models, Automated Model Selection (multilevel analysis, hierarchical model, nested data)

I searched the entire web including this forum on some help on how to use the glmulti package in order to identify the "optimal" fixed part of a mixed model with a given random part. However, I could ...
0
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1answer
23 views

Effects in panel models “individual”, “time” or “twoways”

Panel estimators such the one implemented in the R package plm allow to estimate "individual", ...
0
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0answers
48 views

Fixed effect model is not significant without year dummies

My fixed effect model is insignificant when I don't include the year dummies $$\text{Prob $>$ F} = 0.7769$$ When I try other regressions the model is fine, for example Driscoll & Kraay ...
0
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0answers
48 views

question about multi-level modelling with nested data (R/Stata/SPSS)

I have a dataset composed of observations taken from 16 separate experimental panels, each nested into one of 4 conditions (Treatment A Level 1, Treatment A Level 2, Treatment B Level 1, Treatment B ...
0
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1answer
99 views

What makes the results differ for fixed-effects models vis-à-vis random effects models?

What makes the results differ for fixed-effects models vis-à-vis random effects models? The Cochrane Collaboration's website indicated that two models can produce different results for a meta ...
1
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0answers
43 views

Specific variance covariance structure in lmer

I have a dataset with cluster correlated data; multiple measurement on the same subject (not over time). I am trying to create two different mixed models using lmer in R with two specific variance ...
0
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0answers
19 views

How can dummy variables are able to add in Stata? [duplicate]

I use Stata to estimate Panel data with random effects. In my equation, I have 7 dummy variables which refer to 7 industries. Stata result omit my last dummy variables (industry7), but the rest of ...
0
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0answers
68 views

Is there any model that includes random effects with non-parametric data distribution?

I have a non-parametric (by which I mean non-normal) data distribution. I tried several transformations, but none were helpful. Now, I want to find a model where I can include random effects with the ...
2
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1answer
84 views

Interactions between random effects

I'm considering a mixed-effects model to try to understand factors that influence the number of ticks sampled on wild rodents. My data is nested so that I have one tick count per rodent, multiple ...