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

learn more… | top users | synonyms

0
votes
0answers
36 views

Random variables of mixed models

I am thinking about using mixed models as part of my research, but I am having trouble understanding its application. In particular, I have two somewhat related questions regarding mixed models. ...
0
votes
0answers
33 views

repeated measures ANOVA, crossover trial, R

I am sorry for stating such a question. Unfortunatelly, it's very-very hard to go through all statistics. I have a crossover study design with 3 treatments, 3 periods, and a baseline (covariate). The ...
1
vote
0answers
17 views

Intraclass Correlation Coefficient in mixed model with random slopes

I have the following model m_plot fitted with lme4::lmer with crossed random effects for participants (...
0
votes
0answers
19 views

Empirical Bayes estimates of fixed effects (STATA 12)

I want to calculate the empirical Bayes estimates of the fixed estimates estimated using xtmixed in STATA. I conceptually understand what it is trying to do, but I'm not sure how to implement it in ...
2
votes
0answers
34 views

Using a mixed effects regression model for between-subject design?

I have data from a between-subject experiment, where every subject was assigned to one of the two conditions, and completed varying number of trials (as much as they wanted). Number of trials is ...
0
votes
1answer
47 views

Estimation of school effects using xtmixed (in STATA 12)

I am trying to estimate the school effects on student scores. I ran a model with student effects as a random effect and school effects as fixed effects as follows: xtreg studentscore lagstudentscore ...
0
votes
0answers
6 views

Univariate fixed effect Vs Multivariate model -Negative Covariance, positive parameter estimate, but why?

I am trying to compare the results of two models. The first model looks at y with x as a fixed effect. The second looks at the covariance between x and y. Both models have repeated measures for x ...
2
votes
0answers
17 views

Equivalence of random effects via likelihood and smoothed splines

Some fake data: X = runif(1000) ff = rep(1:10,100) E = rnorm(1000) y = x+e+f f = as.factor(ff) When you fit a model like ...
2
votes
1answer
14 views

conditional independence in repeated measures design

How the responses are independent when conditioned on random effect in repeated measure analysis (linear mixed model)?
2
votes
1answer
77 views

Mixed, repeated measure model specification and results interpretation using LMER in R

I am working with data from a computer task which has 288 total trials, each of which can be categorically classified according to Trial Type, Number of Stimuli, and Probe Location. Because I want to ...
1
vote
2answers
73 views

Log transformation not making data normal

I have a data set with positive skewness when I log tranform it tends to be negatively skewed. Is there any other transformation that I can use or any statistical method works? Thanks!!!
0
votes
0answers
9 views

How to test differences in multiple pairwise distances? With mixed model?

I want to compare two set of variables: X1 = All the pairwise distances between individuals from group A and group ref R X2 = ALL the pairwise distances between individuals from group B and group ...
2
votes
0answers
33 views

Mixed model with lmer: Variance of residuals should give the same as level 1 variance?

I expected that the variance of residuals from a mixed model computed by, for example, lmer should give the same as the residual variance from the summary output. ...
2
votes
0answers
28 views

should I include random effects in a model if they aren't 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 ...
0
votes
0answers
16 views

How adapt MCMC when (constant) weights for oberservations are introduced?

I have the following problem: I have already set up a model and MCMC sampler for a mixed model without weights, i.e., every observation contributes the same amount of information. Now I would like to ...
2
votes
0answers
49 views

Multiple correlated random non-nested intercepts in R

I am trying to estimate a longitudinal model in R in which there are several random intercepts that are correlated with each other, and the data are non-nested. For example, consider a simple ...
3
votes
1answer
46 views

What is the null model for a likelihood ratio test of a within-subjects factor?

Tissue samples were taken from 4 differention locations and repeatedly measured. This was done identically for 3 animals. The research question was: Are there differences in measurement between the ...
0
votes
0answers
14 views

lsmeans: how to have mean and SD from pairwise comparison? [duplicate]

In my previous question, I asked about effect size, but this question is about mean and SD from pairwise comparison generated by lsmeans. I am doing LMM starting from model comparisons. Then after ...
2
votes
0answers
24 views

Heteroskedasticity in a Linear Mixed Model SAS PROC MIXED

Asked a version of this question before but realized it needed some clarification. I have a dataset with identical twin pairs and fraternal twin pairs. I want to examine the relationship between an ...
0
votes
0answers
36 views

Hierarchical ordinal regression (or ranking) with prediction constraints on clusters?

I am interested in predicting an ordered outcome of 0,1,2 or 3 (0<1<2<3) for individual responses in a bunch of different clusters. In each cluster $i$ of size $n_i$ there is a single 3, 2 ...
3
votes
1answer
51 views

How to get proper effect size from LMM?

I now have a problem with effect size of LMM. Someone insisted that I should have effect sizes after p values. I then thought that I can use 'estimate' in the output of pairwise comparison using ...
0
votes
1answer
40 views

How do I choose between a simple and a mixed effect logistic regression?

I have a list of predictor variables to put in to a logistic regression model. How I know that should I do a simple logistic regression (using glm function in R) or ...
-2
votes
0answers
16 views

How to write the SLOBODA trend function in R [migrated]

What is the R code for the following formula?
2
votes
1answer
42 views

Random intercepts as response variables: Is there a name for this method?

I'm trying to find the name of this method (and ultimately a reference). The approach is as follows: 1) Fit a mixed-effect model with a random intercept $$ E(Y_{ij})= ...
1
vote
1answer
60 views

lme() with several within and between (categorical and continuous) subject factors

I am currently trying to analyse data from an experiment of mine and I have done some searching for instructions on the usage of the lme() function for R, since I am looking to analyse my data with a ...
1
vote
1answer
37 views

Choosing between two parameters in a model

I have a few parameters that are related (let's call them X1 and X2), and I want to use whichever one will provide the strongest model. The model has many other parameters. Would I simply be able to ...
1
vote
1answer
30 views

AICc and K for categorical factors and interactions

I am new to multimodel inference. I am trying to create a model that has multiple categorical factors and possible interactions. For example say that my model is... Y ~ X1 + factor(X2) + ...
0
votes
0answers
18 views

Identifying the number of conditions by which variance components will be divided in Generalizability Theory using QME R package

I'm using QME R Package (can be downloaded from here) to calculate Generalizability ...
0
votes
0answers
28 views

To get the overall significant p value, am I on the right track for LMM?

According to this document: http://www.bodowinter.com/tutorial/bw_LME_tutorial2.pdf I can get the overall p-value of fix effects by comparing models that I would like to know to null model. In my ...
1
vote
1answer
30 views

Cluster-randomized trial: mixed model hypothesis testing

I want to estimate the effect of randomly assigned intervention. The outcome is measured at the individual level, but the individuals are assigned to groups which influence eachother a lot, and it is ...
1
vote
0answers
21 views

Reporting Fixed Effects as (partial) correlations?

I'm doing a linear mixed effects analysis in which I'm really only interested in one of the fixed effects. I have several other fixed effects and a random intercept term, but none of them are ...
0
votes
0answers
12 views

Mixed model approach to one sample tests

Is it possible to use the mixed-models approach to test if the mean of a sample is significantly different from zero? I know that mixed models, with crossed random effect for target, and subject, are ...
1
vote
0answers
17 views

Mixed effect models: Why are variance components incongruent with predictive power in presence of hidden interactions?

I have noticed that if there are interactions between hidden variables not in the model, then the variance estimates are inflated greatly compared to the predictive power of the model itself, and I'm ...
1
vote
0answers
40 views

Help with complex model formula in lmer (lme4) for R

Most examples about lmer formula description in R target rather simple study designs. However, sometimes one is confronted with more complex designs and there is no ...
0
votes
1answer
51 views

Mixed model in SPSS with random effect and repeated measures

I am working on analyzing a dataset that involves repeated measures data. The data was previously analyzed by a colleague using custom code written in C++, but I have expanded the dataset and am ...
1
vote
1answer
36 views

Which design is most appropriate?

I have data from an experiment testing a new treatment. Each subject has 4 data points, 2 of the new treatment and 2 of the control. It is like a paired analysis, but instead having 1 observation of ...
1
vote
0answers
34 views

One inflated beta regression with random effects using GAMLSS

I am new to modelling percentage data, and I would be greatfull for some advice. I have proportion data (0,1] on a percentage of money sent by Player B to Player A. Participants received an amount of ...
0
votes
1answer
55 views

Random effect significance in linear mixed model

I have performed LRT: ...
3
votes
1answer
62 views

Design of matrix of contrasts in R

I am doing some post-hoc comparisons (in lme4, but here I'll just present a simple linear model), and I am having a hard time making sure that I am building the right matrix of contrasts to test ...
0
votes
0answers
8 views

Problems running lmer after upgrading to R 3.1.1 [migrated]

I'm running Windows XP, and I recently upgraded to R 3.1.1 and updated all the packages. Oddly, I can't run a lmer on my own data any more. My code worked when I was using R 2.15. I also tried using ...
0
votes
0answers
21 views

How to deal with categorical features.

Recently I am playing in the famous Big-Data website Kaggle. There is a Display Advertising Challenge. In this competition, you are giving a training file which include huge records. the records is ...
1
vote
0answers
20 views

Nagelkerke pseudo-R2 with positive log likelihoods

I'm trying to calculate a pseudo-R2 for linear mixed models using Nagelkerke's method . My understanding is that Nagelkerke's pseudo-R2=1-EXP[(-2/n)(l(B)-l(0))], where l(B) and l(0) are the ...
0
votes
0answers
6 views

Estimation of change and difference in changes for repeated measurements

In an ongoing project concerning life style changes, I can't find the way to study the difference in effects over time between the intervention and the control group. The project is set up as a an ...
1
vote
1answer
148 views

Bias-variance tradeoff in the paired t-test

Suppose we have $K$ subjects and a treatment with two levels, "Before" and "After". A paired t-test is equivalent to fitting a fixed effects ANOVA: $Y = Subject + Treatment +\epsilon$ It is also ...
0
votes
0answers
18 views

Model selection in nlme's

I can think of four ways to perform model selection nlme's: LRT, AIC, ...
2
votes
2answers
57 views

Homogeneity of variance in linear mixed model

I am confused by the assumption of homogeneity of variance of the Linear mixed model. Does homogeneity of variance equal to homogeneity of error? May I know is the homogeneity of variance referring ...
0
votes
0answers
32 views

Correct use of Mixed model for random block design?

I have the following experimental design (random block design). Block with 4 Treatments (A, B, C, D) repeated at 4 different sites. Treatments were applied to individuals of different species (factor ...
1
vote
0answers
22 views

Specific contrasts in mixed model with interaction

I have a dataset consisting of two groups tested across three days. Therefore I run a linear mixed model as follows: ...
1
vote
1answer
64 views

Mixed models with R - convert from SAS code

I have this SAS code running a mixed model: ...
2
votes
0answers
42 views

R model.matrix and makeContrast. Understanding model and possible contrast

I have measurements from 12 mice, grouped in two conditions. I each mouse I have measurements from 4 tissues. The design is not balanced, 5 mice in condition1 and 7 in condition2. After reading the ...