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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12 views

gam models with random effect R

I am modeling fishery CPUE as a function of a number of a number of covariates using a GAM approach that includes fixed and random effects. I understand that there are limitations with regards to ...
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0answers
12 views

2SLS random effect heteroscedasticity autocorrelation [on hold]

I need to estimate a model using 2SLS with panel data because I have an endogenous variable. Hausman tells me to choose a random effect specification so I should use the ...
2
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1answer
56 views

Panel data regression of crowdfunding projects

I never did something with panel data before, and could use some help. I have data of 173 crowdfunding projects, measured at four different time points, with %funded, if the project contains a video, ...
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0answers
13 views

Longitudinal analysis time invariant outcome

I want to assess the relationship between a predictor measured at multiple (4) time points and a dependent variable measured at a single time point. What would be most appropriate analytic strategy ...
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1answer
127 views

How Stata estimates a random effects for an unbalanced panel

I have estimated a random effects model with an unbalanced panel. I would like to know if Stata gives each individual the same weight when estimating the coefficients or whether each individual is ...
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0answers
11 views

Conditioning on random effects?

To protect the innocent, I'm going to fabricate an example. Suppose I've got 100 musicians: 50 attended school A, 50 attended school B. I'm interested in determining which school tends to produce ...
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0answers
22 views

Get predictions from mgcv:gam model with a new level in a random effect smooth

Is there a way to get a prediction from an gam model (from package mgcv) that contains random effect smooths, where the new data contains a level of the random effect that didn't exist in the training ...
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1answer
41 views

How to choose between fixed effects and random effect meta-analysis

In meta-analysis packages, both fixed effects and random effects models are available. How do one choose between these 2 models? Since one is assessing different studies, should one not choose random ...
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0answers
44 views

Random forest for panel data

I have a dataset with observations from about 50 countries and 20 years. My dependent variable is binary and I was wondering if I could use random forest to do out-of-sample predictions. My problem ...
2
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1answer
45 views

latent class analysis which modifies “given a certain class, probability for a respondent to show observed response”

Context I am trying to model a latent class model, which i give a priori restriction for the class-specific probabilities I'm using an SP data, for which respondents chose an alternative in 3 ...
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0answers
29 views

Sample size calculation for crossover design using dependent t-test - role of $\rho$?

I am trying to do a sample size calculation for a 2x2 crossover study. I have access to previous studies whereby I can get covariate adjusted estimates of the variability of the responses (from a ...
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0answers
16 views

How to define random effects for nested LMM

I investigated the behaviour of 125 individuals in 25 groups of 5 individuals across 4 days. The data is structured as follows (only showing 2 dummy groups and 2 individuals in each group): ...
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0answers
8 views

Is there an supervised learning method (a classifier) that can account for unobserved heterogeneity like a mixed logit can?

I'm just starting to teach myself various machine learning techniques. My background is in more "classical" statistics. I've got an analysis that I've done using a mixed logit to predict linkage in ...
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8 views

Random effects for analysis on pair of sites

I'm currently working on an analysis where I'm interested in the interaction between two sites. On each combination of my site I have a symmetrical variable (here a genetic correlation indicator ...
3
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1answer
29 views

different results from lme and lmer function

I am fitting a random slope and random intercept model using R. I used both lme and lmer funciton for the same model. However I got different results as shown below (different variance component ...
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1answer
32 views

Interpretation of model comparison with random slopes using lmer in R

I have a large data set with repeated measurements of same blood value (co) (2 to 7 measurements per patient). Each measurement is coupled with time which is the time interval between surgical ...
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0answers
40 views

Random slope and random intercept correlation at every level of X

Lets say individuals are nested within each ID and I am trying to a predict level 1 outcome Y from a level 1 predictor X1 or X2 with random slopes and intercepts. X1 and X2 are equivalent to each ...
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2answers
145 views

Random slope and intercept correlation. Not consistent in output vs manual calculation

Lets say individuals are nested within each ID and I am trying to a predict level 1 outcome Y from a level 1 predictor X with random slopes and intercepts. Using the nlme package in R, I ran the ...
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0answers
16 views

syntax rules for specifying multiple random effects in lme4 [duplicate]

I’m trying to get my head around the lme4 syntax for multiple random effects. I know there is lots of information out there on the topic, but I still haven’t found a source that provides clear ...
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1answer
46 views

Fixed/Random effects model

I am trying to understand/visualize it in my head how fixed/random effects models work. Can someone explain how can I infer something about the population from which I drew the sample with random ...
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0answers
41 views

Multilevel (Hierarchical) Models - data set example

I am currently trying to find an example that will use a Miltilevel/Hierarchical Model. The data set I am currently looking at is student "success" in a post-test regarding STD education. The data has ...
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0answers
17 views

Autocorrelation in random effects model

I am doing a random effects regression that has first order autocorrelation. When I use a robust method, my results turn insignificant. But if I exclude time dummies from the robust regression, my ...
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0answers
34 views

Formula for pooling variance components across imputations in mixed effects models?

I've had no luck with this question. I see here that someone has asked a very good question about combining confidence intervals. I haven't been able to find anything about even combining the ...
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1answer
77 views

Using individuals as a random effect

I would like to compare the average gene expression of three different genes in three different brain structures under two different conditions (see the table below). All means have been ...
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0answers
18 views

Effect size for two-sample test with complications such as multiple data per subject

I have a simple hypothesis, that my dependent variable depends on a two-level factor Condition. Ideally I would like get a measure of Cohen's D, with confidence interval. The problem is my model is ...
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0answers
30 views

Concerns regarding correlation structures and random variance using lme

I’m modeling some variables repeatedly measured over a three months period for a total of 300 individuals. These variables (e.g. activity) were measured at three different time scales: daily (90 ...
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0answers
10 views

What method to use for panel data: Impact of x on stock price volatility?

I have a Panel Data set for 60 companies, 10 years i.e. 600 observations. What I want to do is investigate whether the publication of a specific number (let's assume earnings per share, EPS) has an ...
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0answers
38 views

Glmer random effects model vs. dummy-coded fixed effects

I'm trying to analyze the data from an experiment I conducted, and could use some guidance in relation to fixed vs. random effects. The experiment was related to risk-seeking behavior in the context ...
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3answers
60 views

Need to model Panel Data

I have been given a assignment in which I fit various panel data models on a given set of data, and explain the pros and cons of each model. My data has 3 dependent variables, 6 independent variables ...
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0answers
15 views

Why are results different from R mixed effect logistic regression models with nested random effects?

I have a dichotomous outcome on 2500 individuals. From 18 geographical areas, and many households nested within areas. I need to assess the association between various predictors and my outcome, ...
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1answer
34 views

Mixed models and longitudinal studies: Is it ok to specify a random slope with time as a categorical?

My model is currently setup as follows either with just random intercepts: ...
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0answers
40 views

Linear mixed model with two correlated dependent variables

I'm using the swissmunicipalities dataset in the package sampling of R. I consider two correlated dependent variables, the population between 40 and 65 (Pop4065), and the population aged 65 or more ...
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0answers
17 views

Using Random Effects As Independent Variables in Other Models?

I was having a discussion with a colleague yesterday about an analysis he was doing with some student achievement data. We got into a discussion of value added models (VAM), which in my understanding ...
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0answers
84 views

95% CI in nonlinear mixed-effect model {lme4} with two or more crossed random effects

I have fisheries-independent data and am interesting in estimating maturity patterns across 50 lakes that are sampled (with bias) by 4 types of gear-collections. The sampling pattern is very ...
2
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1answer
25 views

Random Effect Model and Response Surface Methodology

In Design and analysis of experiment , Random effect is defined as : An experimenter is frequently interested in a factor that has a large number of possible levels. If the experimenters randomly ...
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0answers
19 views

Help on calculating variance for randon mixed effects

This is related to the post here: Understanding the variance of random effects in lmer() models I'm trying to calculate that proof explicitly and am missing something. The setup is we have a random ...
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1answer
88 views

Nested random effects and interaction terms in lme4

I have a data set containing various vegetation and geomorphic variables sampled in 3 distances on both sides of 43 drainage ...
2
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0answers
44 views

Impute missing data for mixed effects models?

Although I will not provide a reference, because I cannot recall where I did read it, I have several times read or heard that missing data is accommodated automatically in mixed models. Can anyone ...
13
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1answer
372 views

Why do mixed effects models resolve dependency?

Say we're interested in how student exam grades are affected by the number of hours that those students study. To explore this relationship, we could run the following linear regression: $$ ...
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0answers
24 views

Fixed Effect vs Random Effect Output

I am currently a statistics student taking an designing experiments class. At this point I feel I am getting a good grip on when to choose between a fixed effects, random effects, and mixed effects ...
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0answers
23 views

random effect for mixed model

I have ran mixed model with random effect. I have design 4 models iteratively following Zuur et al. the simplest one, and then I have added the other main and interaction effect (all with 1|subject as ...
0
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0answers
59 views

Nested ANOVA with 3 random effects and unbalanced design

I would like to run a nested ANOVA to test three random effects (secteur, loc nested in secteur, site nested in loc) on the variable A. The design is unbalanced so I used lmer instead of aov. However, ...
0
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1answer
59 views

lmer syntax for a two-way model with one fixed and one random factor [closed]

Please could anyone tell me if my R code is correct? I have a two-way model with one fixed factor, habitat, and one random factor, site. The code I am using is: ...
0
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0answers
14 views

Implications of using fixed effects to account for hierarchical data structure

I am currently implementing a hidden Markov model in R, using the msm package. The data I am using are drawn from a cluster-randomized trial; i.e. there is a ...
0
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0answers
17 views

F test in random effects panel regression

I'm using random effects panel regression and I've 3 covariates not statistically significant and I want to test if the three parameters associated with those covariates are jointly equal to 0. Could ...
0
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1answer
26 views

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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0answers
19 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
109 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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0answers
31 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
37 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 ...