Refers to a class of models developed to account for correlation that may occur within nested data.

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6 views

Zero-inflated mixed models with two-stage fitting

Zero-inflated models have a count component (Poisson/Neg. Binomial) and a zero component (logistic regression part). glmmADMB supports the zero-inflation feature but only through estimating a ...
1
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2answers
52 views

Random intercept turns insignificant - Interpretation

I am running a intercept model with intercepts varying across people in R. My independent variables are all numeric variables. My question is a general one: I saw already that it can happen that my ...
0
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0answers
21 views

Box cox for mixed models in R

Consider a mixed model generated using the lme function in R. How can I consider the Box-cox transformations of this model in R? I have seen similar questions being asked before but they did not give ...
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0answers
15 views

mixed models with random effect for repeated measures [closed]

I have run an experiment to investigate face recognition accuracy. there were three within subject variables (duration with two levels; intensity with 5 levels; emotion with 4 levels) and a between ...
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0answers
2 views

Error appears for klm function in Latent Growth Mixture Modelling

I am trying to run the klm function for a simple dataset with n=59 and body weight measured at 8 time points. The commands used were: weitraj <- read.table("weitraj.csv",header=T,sep=";") ...
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1answer
18 views

Addressing a mixed design analysis of variance when only one of the two groups went under repeated measures?

so essentially I have 2 groups (A and B, A is a control, and B can be referred to the "expert" group), and group B went under a specific training program (two times points, one prior to the training, ...
3
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1answer
66 views

R glmer.nb output. How to get $\hat{\theta}$?

I would like to obtain estimated $\theta$ from glmer.nb function in lme4 package. In my understanding this function fits the model: $$ Y_{ij}|\boldsymbol{B}_{i}=\boldsymbol{b}_i \overset{ind.}{\sim} ...
2
votes
1answer
26 views

multiple imputation of longitudinal, time-unstructured data

I have a longitudinal dataset of radiation exposures of an occupational cohort. A percentage of the exposure values are missing and I would like to multiply impute the missing values (it is one option ...
1
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0answers
19 views

Holding random effects variance constant in PROC MIXED vs lmer [migrated]

I was wondering if it is possible to hold random effects variances constant in R's lme or ...
0
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0answers
24 views

In the optimal model, do I need to change 'REML=FALSE' to 'REML=TRUE'?

I did the model comparison using these three models: ...
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0answers
8 views

Can you compare groups over time if data has been collected at different time points?

If have two groups with follow-up measures of a normally-distributed continues outcome measure. One group is measured at 4 time points with unequal intervals. The other group is measured at 7 time ...
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0answers
7 views

effect of accounting clustering

I am analyzing a data where measurements are taken at pups and these pups are nested to their corresponding mom (rats). When I accounted for clustering, factors diet, drug, and interaction of diet ...
0
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0answers
43 views

Problems fitting a repeated measures model (time series analysis): fixed-effect model matrix is rank deficient so dropping 1 column / coefficient

I’m trying to fit a model with 4 parameters by not including all the interactions, just including the interaction which got biological sense. This is my data ...
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0answers
25 views

Syntax for a logistic multilevel analysis in R?

I am looking for advice on what package and syntax to use in R for modelling a logistic multilevel analysis with variables as follows: Dependent variable (binary) = 0 or 1 Independent variable 1 ...
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0answers
8 views

How to include replicates in linear mixed models in SPSS?

In my study I had 11 radiocollared hares which i gave 3 different treatments. one treatment was the release of stressed hare sounds in combi with red fox sounds, the second treatment was sheepsound ...
0
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1answer
20 views

Mixed model analysis: random vs repeated statement

I have data from a longitudinal parallel groups study where there are 46 subjects randomized to 1 of 5 treatment groups, each subject with roughly 13 observations over time on a given outcome measure. ...
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0answers
14 views

How many random effects to specify in glmmADMB?

I am not sure if I could use 4 random effects in a glmmADMB model. According to Bolker et al. (TREE 24: 127-131, 2009) when there are more than 3 random effects MCMC should be used. However, I do not ...
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0answers
8 views

Which type of test is better to compare difference of one independent variable (3 groups and one control) on a repeated measures experiment?

I have a group of 100 classroom participants, all who have taken a course that included four different lessons (from a single unit). Three lessons were taught each using a different form of ...
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2answers
116 views

Is it correct to model percent change as a continuous variable?

I have data from a longitudinal study where there are 46 subjects, each with 13 observations over time on a given outcome measure. A prior analysis conducted on this data performed a linear mixed ...
1
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1answer
23 views

power for mixed-effects model

I wonder if there is a simple way of calculating an achieved power for a mixed-effects model. The fixed effects are the intercept and a slope. The random effect is for the intercept at the two levels ...
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1answer
33 views

Comparison between a multilevel and an unpooled model

Suppose we have fitted two models: a multilevel model and an unpooled model: m1=lmer(y~x+(1|group)) m2=lm(y~x+factor(group)-1) How can I understand which ...
1
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0answers
10 views

OpenBUGS and coda library

I've fitted a hierarchical model with OpenBUGS using R2WinBUGS package of R. Now, I'd like to use the functions contained in the ...
0
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0answers
4 views

Redundant parameters in multilevel models

I am a bit confused about the use of redundant parameters in multilevel models in order to speed the convergence of the Gibbs sampler. I don't understand how the model should be reparametrized. ...
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0answers
16 views

Observer agreement in multilevel binary data

I have a set of participants indicating with drawn circles where they experience pain on a manikin of the human body. These drawings were then independently rated by two human raters: each of 29 body ...
5
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1answer
56 views

Real motivation for using mixed effect models, and when to use them and when not to

My question might sound naïve, but despite my internet search, I wasn't able to find a satisfactory answer. I've been introduced to linear regression, linear fixed effect and linear mixed effect ...
2
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1answer
54 views

MATLAB2014b `fitglme` causes error on intermediate results

((This post is a duplicate from Stack Exchange as there was no response there)) MATLAB R2014b's library function fitglme is acting up. It seems to be producing ...
2
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1answer
29 views

What is the nature of the normality assumption in models for longitudinal data?

I'm working on a longitudinal dataset to which I've been fitting non-linear mixed effects model in R. Regarding normality, I have a few questions: Can I assume that a longitudinal data is normally ...
0
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0answers
37 views

LMM: How do I calculate a standard deviation on the variance explained by fixed effects?

So, building a LMM with the lmer function in lme4, you get the variance explained by the variance component. ...
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0answers
45 views

What is BLUE and BLUP and REML? Explain in a very layman language and with simple example [duplicate]

It will be better if you explain in plain language. With a very simple example could you please explain without any complicated terms. Along with it random effect, fixed effect and mixed model effect. ...
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0answers
18 views

Checking assumptions of a mixed model

I was wondering how to check the assumptions of a mixed model in R. Suppose I fitted a model with lmer. Is this what I should do? ...
1
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0answers
22 views

Significant cross-level interaction despite lack of variance in level-1 slopes

I have a logistic HLM model with one level-1 predictor and without level-2 predictors. Random variance components are significant for intercepts, but far from significant (p>.5) for slopes. In my ...
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0answers
32 views

Model formulation of mixed models: from R (nlme and gamm) syntax to mathematical

I have finalized two types of models, a linear mixed effects model and a generalized additive model for my data set. I have read and understood how it works and how to choose which model would be the ...
0
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0answers
8 views

How to interpret change in intercept when a random effect is added to a mixed model

I have a fixed effect model, constructed in SAS using Proc Mixed as: proc mixed data= etc...; model ca = p t s /solution; run; which yields the following ...
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0answers
35 views

Subject-specific graphics for repeated-measure design

I am doing linear mixed effect modelling testing the effect of treatment on pitch, with ...
2
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1answer
407 views

Obtaining adjusted (predicted) proportions with lme4 - using the glmer-function

I aim to estimate the annual proportion of patients (% of patients) that are smokers in a population whose age and sex must be taken into account. In other words, I want to calculate the adjusted ...
1
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0answers
15 views

Correct df in longitudinal linear mixed model?

I am having trouble understanding how to correctly apply a linear mixed model to my data to measure the effect of wifi exposure. 4 beehives contained sensors collecting data on temperature (DHT22_t, ...
2
votes
1answer
49 views

Mixed effect modelling with multiple, nested random variable

Goal: comparing pitch (Hz) on three types of words Dependent variable: Hz Fixed predictor variable: word-type, points (measurements taken from five points on each token, to capture Hz change within ...
1
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0answers
28 views

what is the meaning and purpose of modeling a data?

Background: I collected a whole years access logs of my website, counted visit frequency for every user, and the numbers of user at each unique frequency, I got a distribution: $n_w \tilde\ D_w(f_w ; ...
2
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0answers
34 views

Multiple comparisons for variance structure in R lme fit

How can I compare variances for different levels of a factor in a mixed effect model? I'm fitting a mixed effects model (in R using the ...
1
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0answers
48 views

Comparison of crossed random effects (mixed models): lmer vs. MCMCglmm

I read that lmer can handle independent (often labeled as crossed) random effects in mixed models. It seems to be possible with MCMCglmm as long as groups for the random effects are uniquely labeled. ...
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0answers
22 views

How to analyze data with DV only measured at the group level and moderator measured at the individual level?

This may be a relatively simpleton kind of question to ask, as this forum seems to be rather statistically sophisticated, but I'm rather mixed up right now. I ran a study that involved individuals ...
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0answers
12 views

How to summarize a series of cumulative variables and extract their contributions to the variation explained by the summarized variable?

I want to use a mixed logistic regression. My explanatory variables are ten variables corresponding to cumulative variables measuring the same value over the 10 years preceding the year of interest, ...
0
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0answers
8 views

Extracting random variable coefficients from model averaged objects

I'd like to extract the coefficients of the random effects from a model averaged object of class averaging created in the package ...
0
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0answers
26 views

unbalanced groups in mixed design ANOVA

I want to perform a mixed design ANOVA. Time is the within subjects factor and the between subjects factor is Borderline, which is a categorical variable (borderline yes or no). There are 30 people ...
1
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0answers
18 views

Poisson regression with underdispersed and truncuated/censored upper bound

I'm analysing data from an experiment in which participants, over a number of trials, were presented with 8 boxes - 7 containing gold coins, and 1 containing a pirate. Their task was to open as many ...
2
votes
1answer
49 views

Interpret effect of adding random effects to ordinal regression (R - ordinal package - clmm)

I know there are already lots of questions around this topic (especially this one and this one) but I haven't really seen anything that directly helps me (It will be obvious I'm not a great ...
1
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1answer
26 views

ANOVA, mixed model, or something else, for ordinal groups with a continuous outcome?

Possibly a simple / daft question so apologies in advance... Suppose I have several independent observations of a continuous variable, like height. The observations are grouped, and I would like to ...
0
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0answers
5 views

Variance component analysis nlme

Is there a way to carry a variance component analysis using nlme or lme4 packages and how would I calculate the percentage of variance that is attributable to the random effects? For example, my ...
1
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0answers
17 views

How to specify random effects in lmer

I have 3 groups of animals that are divided into 3 subgroups, each subgroup contains animals that are specific to each subgroup (there is no same animal in two groups). How do I specify random effects ...
4
votes
1answer
24 views

What to do about very unstable mixed-effects models

I'm working on some poisson mixed effects models for an interrupted time series analysis, and I'm running into two frequent errors. The first I've posted on Stack Overflow, as it appears to be purely ...