All Questions
2,982 questions
13
votes
1
answer
3k
views
Managing high autocorrelation in MCMC
I'm building a rather complex hierarchical Bayesian model for a meta-analysis using R and JAGS. Simplifying a bit, the two key levels of the model have
$$ y_{ij} = \alpha_j + \epsilon_i$$
$$\alpha_j =...
- 802
13
votes
2
answers
7k
views
How many observations do you need within each level of a random factor to fit a random effect?
I'm trying to analyse some data from a set of bird surveys. My response variable is "bird abundance", which is the number of birds counted over a five-minute period. These five-minute counts were ...
- 1,215
13
votes
2
answers
5k
views
Are $R^2$ for GLMM useful for modelers but not necessarily for readers?
The short version:
1)Are there any published critiques of the use of $R^2$ for GLMMs, in particular the popular approach of Nakagawa & Schielzeth (2013) A general and simple method for obtaining $...
- 2,173
13
votes
2
answers
841
views
MCMC converging to a single value?
I'm trying to fit a hierarchical model using jags, and the rjags package. My outcome variable is y, which is a sequence of bernoulli trials. I have 38 human subjects which are performing under two ...
- 2,457
13
votes
1
answer
4k
views
Varying dispersion parameter (=dispformula) in glmmTMB in R to account for heteroscedasticity that originates from one predictor
I struggle with understanding the dispersion model and dispersion parameter of glmmTMB , and could not find answers anywhere.
I constructed a GLMM using ...
- 131
12
votes
4
answers
2k
views
Why is it OK to model demographics as random effects in bayesian multilevel models?
In Bayesian multilevel models (with, say, people nested within congressional districts) I sometimes see individual level demographic variables like race modeled as random effects.
So here’s a slightly ...
- 6,020
12
votes
1
answer
6k
views
Multiple Membership vs Crossed Random Effects
I see that there is a multiple-membership tag, but I can't find a good explanation of what a multiple membership model is, or how to go about fitting one.
In my limited understanding, it seem very ...
- 3,942
12
votes
2
answers
10k
views
What is hierarchical prior in Bayesian statistics?
What are hierarchical priors?
How do they differ from the general concept of priors?
- 6,429
12
votes
2
answers
4k
views
Highly irregular time series
I have data for the population of a number of different fish, sampled over a period of about 5 years, but in a very irregular pattern. Sometimes there are months between samples, sometimes there are ...
- 128k
12
votes
1
answer
4k
views
Multilevel multivariate meta-regression
Background:
I'd like to conduct a meta-regression using studies which have (1) several outcomes/constructs (= multivariate) and (2) multiple effect sizes for every of these outcomes because of ...
- 205
12
votes
1
answer
1k
views
Notation for multilevel modeling
The formula one needs to specify for training a multilevel model (using lmer from lme4 R ...
- 876
12
votes
3
answers
26k
views
Fixed vs Random Effects
I have very recently started learning about Generalised Linear Mixed Models and was using R to explore what difference it makes to treat group membership as either fixed or random effect. In ...
- 191
12
votes
2
answers
4k
views
Should I bootstrap at the cluster level or the individual level?
I have a survival model with patients nested in hospitals that includes a random-effect for the hospitals. The random effect is gamma-distributed, and I am trying to report the 'relevance' of this ...
- 550
12
votes
2
answers
3k
views
What is the "variance component parameter" in mixed effect model?
On page 12 of Bates' book on mixed effect model, he describes the model as follows:
Near the end of the screenshot, he mentions the
relative covariance factor $\Lambda_{\theta}$, depending on the ...
- 4,610
12
votes
1
answer
12k
views
Why do Anova( ) and drop1( ) provided different answers for GLMMs?
I have a GLMM of the form:
lmer(present? ~ factor1 + factor2 + continuous + factor1*continuous +
(1 | factor3), family=binomial)
When I use <...
- 624
12
votes
1
answer
3k
views
Stratified classification with random forests (or another classifier)
So, I've got a matrix of about 60 x 1000. I'm looking at it as 60 objects with 1000 features; the 60 objects are grouped into 3 classes (a,b,c). 20 objects in each class, and we know the true ...
- 508
12
votes
2
answers
23k
views
How to test for overdispersion in Poisson GLMM with lmer() in R?
I have the following model:
> model1<-lmer(aph.remain~sMFS1+sAG1+sSHDI1+sbare+season+crop
+(1|landscape),family=poisson)
...and this is the summary ...
- 711
12
votes
1
answer
2k
views
Overdispersion and modeling alternatives in Poisson random effect models with offsets
I have run into a number of practical questions when modeling count data from experimental research using a within-subject experiment. I briefly describe the experiment, data, and what I have done so ...
- 6,724
11
votes
2
answers
11k
views
Conditional vs. Marginal models
I have data with an outcome of 0 or 1 (binary) representing success or failure. I also have two comparison groups (Treatment vs. Control). Each subject in the study contributed 2 observations (the ...
- 815
11
votes
3
answers
19k
views
Generalized linear mixed models: model selection
This question/topic came up in a discussion with a colleague and I was looking for some opinions on this:
I am modeling some data using a random effects logistic regression, more precisely a random ...
- 307
11
votes
2
answers
11k
views
Effect size in GLMM
In the output of a GLMM, using a binary variable as response variable and continuous variables as explanatory variables [family = binomial(link="logit")], I obtain, for each variable, an estimate ...
- 637
11
votes
1
answer
7k
views
Is it a must to include a random slope in a mixed model?
I am learning about fitting mixed models and I find when it is justified to include or exclude a random slope rather confusing.
Some tutorials suggest that although the maximal random structure should ...
- 125
11
votes
1
answer
8k
views
How to conduct a multilevel model/regression for panel data in Python?
I have yearly data over time (longitudinal data) with repeated measures for many of the subjects. I think I need multilevel modeling/regressions to deal with sure-to-be correlated clusters of ...
- 237
11
votes
1
answer
3k
views
Regression: Interaction Effects vs Random Effects
I'm struggling to understand the difference between creating an interaction effect in linear regression vs a random effect. Both allow the algorithm to identify a different slope for a coefficient ...
- 121
11
votes
1
answer
12k
views
Model selection: can I compare the AIC from models of count data between linear and poisson models?
I am modeling count data (with offset / exposure parameter). My modeling strategy is use of a Poisson model and a negative binomial regression model. I compare model AICs, which are about -760 for my ...
- 6,724
11
votes
2
answers
2k
views
Hierarchical models for multiple comparisons - multiple outcomes context
I've just been (re-)reading Gelman's Why we (usually) don't have to worry about multiple comparisons. In particular the section "Multiple outcomes and other challenges" mentions using a hierarchical ...
- 3,359
11
votes
1
answer
876
views
Torn between PET-PEESE and multilevel approaches to meta-analysis: is there a happy medium?
I am currently working on a meta-analysis, for which I need to analyze multiple effect sizes nested within samples. I am partial to Cheung's (2014) three-level meta-analysis approach to meta-analyzing ...
- 5,566
11
votes
1
answer
3k
views
Likelihood and estimates for mixed effects Logistic regression
First let's simulate some data for a logistic regression with fixed and random parts:
...
- 631
11
votes
2
answers
3k
views
Generalized Linear Mixed Models: Diagnostics
I have a random intercept logistic regression (due to repeated measurements) and I would like to do some diagnostics, specifically concerning outliers and influential observations.
I looked at ...
- 307
10
votes
4
answers
9k
views
How to test random effects in a multilevel model in R
I have been reading a good book called Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence by Judith Singer and John Willet. The book shows that by modeling in 2 levels, we can ...
- 517
10
votes
1
answer
5k
views
Should I exclude random effects from a model if they are not statistically significant?
Should I include random effects in a model even if they aren't statistically significant? I have a repeated measures experimental design, in which each individual experiences three different ...
- 123
10
votes
1
answer
25k
views
Use of ICC in multilevel modelling
Ive just recently started learning about the ICC and multilevel models and I've been told that one way to determine whether a MLM is warranted is by checking the size of the ICC. I'm struggling to ...
- 2,361
10
votes
3
answers
2k
views
Why the exchangeability of random variables is essential in hierarchical bayesian models?
Why the exchangeability of random variables is essential for the hierarchical Bayesian modeling?
- 3,089
10
votes
2
answers
229
views
What is the difference between autocorrelated residuals and controlling for the previous time point in mixed effects models?
I have several dozen observations from about 100 people who participated in an ecological momentary assessment study. I am using mixed effects models to estimate the effect of $X_{t-1}$ on $Y$.
...
- 235
10
votes
1
answer
3k
views
Can I fit a mixed model with subjects that only have 1 observation?
I have a very large dataset where I have repeated measurements over time for individual locations. Some locations might have 10 data points and some locations have only 1 data point. I fit a mixed ...
- 265
10
votes
1
answer
16k
views
Help interpreting count data GLMM using lme4 glmer and glmer.nb - Negative binomial versus Poisson
I have some questions regarding specification and interpretation of GLMMs. 3 questions are definitely statistical and 2 are more specifically about R. I am posting here because ultimately I think the ...
- 103
10
votes
1
answer
6k
views
Clustered standard errors and multi-level models
Stata allows estimating clustered standard errors in models with fixed effects but not in models random effects? Why is this?
By clustered standard errors, I mean clustering as done by stata's ...
- 958
10
votes
1
answer
816
views
Parsimonious Mixed Models
I recently read a paper on the trimming of random effect structure by Bates, Kliegle, Vasishth and Baayen (2015). My understanding is that the Parsimonious Mixed Model they proposed mainly follows the ...
- 373
10
votes
3
answers
407
views
Bacteria picked up on fingers after multiple surface contacts: non-normal data, repeated measures, crossed participants
Intro
I have participants who are repeatedly touching contaminated surfaces with E. coli in two conditions (A=wearing gloves, B=no gloves). I want to know if there's a difference between the amount ...
- 789
10
votes
2
answers
3k
views
Borrowing strength
What are the principles of Borrowing Strength?
What does it mean in terms of estimating parameters for hierarchical models?
Where can this information can be read from?
- 661
10
votes
2
answers
21k
views
Should I use Poisson distribution for non-integer, count-like data?
It's my first question here, I hope I'll ask it correctly. I am trying to find out how to analyse non-integer, count data (yes!). I am looking at the effect of a given treatment on habitat suitability ...
10
votes
1
answer
2k
views
Latent variables, overparameterization and MCMC convergence in bayesian models
Sometimes I have a large number of latent variables in a Bayesian hierarchical model to which, but I am only interested in estimating projected transformations of those latent variables (for example, ...
- 2,724
9
votes
3
answers
8k
views
Can I test for correlation between variables before standardize them?
What I want to do is to construct GLMM's to evaluate resource selection, and I have a set of variables (some representing distances and others representing % of land cover).
Can I test for ...
- 637
9
votes
4
answers
2k
views
Is it mandatory to subset your data to validate a model?
I'm having a hard time getting on the same page as my supervisor when it comes to validating my model. I have analyzed the residues (observed against the fitted values) and I used this as an argument ...
- 213
9
votes
2
answers
4k
views
Why not always use generalized estimating equations (GEE) instead of linear mixed models?
I read about generalized estimating equations (GEE) here, here and at other sites.
It is mentioned in first of above links that "the parameter estimates are nearly identical" for linear ...
- 10.2k
9
votes
4
answers
1k
views
Standard algorithms for doing hierarchical linear regression?
Are there standard algorithms (as opposed to programs) for doing hierarchical linear regression? Do people usually just do MCMC or are there more specialized, perhaps partially closed form, algorithms?...
- 4,590
9
votes
2
answers
912
views
9
votes
1
answer
13k
views
How to account for repeated measures in glmer?
My design is as follows.
$y$ is Bernoulli response
$x_1$ is a continuous variable
$x_2$ is a categorical (factor) variable with two levels
The experiment is completely within subjects. That is, ...
- 101
9
votes
2
answers
2k
views
Does a distance have to be a "metric" for an hierarchical clustering to be valid on it?
Let us say that we define a distance, which is not a metric, between N items.
Based on this distance we then use an Agglomerative hierarchical clustering.
Can we use each of the known algorithm (...
- 21.9k
9
votes
1
answer
4k
views
Resolving heteroscedasticity in Poisson GLMM
I have long-term collection data, and I'd like to test, whether the number of animals collected is influenced by weather effects. My model looks like below:
...
- 162