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

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2 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 2) Use the estimated random intercepts as the ...
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1answer
20 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 ...
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0answers
18 views

Accounting for Different Levels of Twin Relatedness in SAS

I have a dataset with a number of identical twin pairs and fraternal twin pairs. I want to examine the relationship between two variables (let's call them INDEPENDENT and DEPENDENT). However, I ...
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0answers
18 views

Cannot get the p values from afex package [on hold]

According to this website: http://www.psychologie.uni-freiburg.de/Members/singmann/R/afex I have run: ...
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1answer
32 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
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1answer
27 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) + ...
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0answers
13 views
+50

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 ...
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0answers
24 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 ...
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0answers
16 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 ...
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0answers
16 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 ...
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0answers
10 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 ...
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0answers
13 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 ...
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0answers
38 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
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1answer
29 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 ...
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1answer
32 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 ...
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0answers
22 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 ...
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1answer
45 views

Random effect significance in linear mixed model

I have performed LRT: ...
3
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1answer
53 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 ...
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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 ...
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0answers
19 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 ...
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0answers
18 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 ...
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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 ...
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0answers
51 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 $ It is also equivalent ...
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0answers
17 views

Model selection in nlme's

I can think of four ways to perform model selection nlme's: LRT, AIC, ...
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2answers
48 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 ...
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0answers
28 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 ...
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0answers
20 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: ...
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1answer
53 views

Mixed models with R - convert from SAS code

I have this SAS code running a mixed model: ...
2
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0answers
28 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 ...
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2answers
45 views

How to analyze this messy design?

I need to analyze a data-set, with a very messy design, I am not sure how. I will try to make it simple. A new kind of stitches was invented, and is tested vs. 2 old kind of stitches. I will call ...
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0answers
22 views

3 way mixed ANOVA & pairwise comparisons effect size

im in desperate need of some help for my data analysis assignment! I have a 3 way mixed anova- where I have 2 sig main effects, one significant two way interaction and the sig three way ...
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0answers
9 views

`bGAMM` and other `GMMboost` algorithms for large data sets

Regularized generalized linear mixed models and generalized additive mixed models are exactly what I need. I'm an R user, so it looks like bGAMM and maybe ...
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0answers
18 views

expected values determined with model parameters estimated from a nlme analysis

I'm kind new into nonlinear mixed model theory and I've seen that you cannot determine expected values of your response variable by simply inserting the estimated parameters into your model equation, ...
1
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1answer
34 views

Mixed model interaction (covariate+factor): How to interpret posthoc table output in R package phia?

In R, using package lme4, I have used the following 2 mixed models to determine I have a signifacnt interaction between a covariate (continous, normally distributed) and a factor (three levels: ...
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0answers
15 views

SAS syntax to find differences in regards to a control treatment

I am working with a data set of bacterial cell counts, using flow cytometry. I recorded the cell number in 3 different species of bacteria, all treated with 3 different compounds (L-aspartic acid, ...
0
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1answer
39 views

After trying various optimzers, model simplification running more iterations, when fitting GLMMs, R still produces warning messages

I am trying to fit GLMM's to my data using the glmer function available in R's lme4 package. The data is available at: https://onedrive.live.com/redir?resid=1B727FC7180E87DF%21118 I keep getting ...
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0answers
42 views

Mixed linear models in R, help with nested terms and procedure

This is my first post, so sorry if it not optimally written. I have a paired samples at two time points in two groups, undergoing the same intervention. I want to test the effect of my intervention on ...
1
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1answer
47 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 ...
0
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1answer
66 views

Number of observations in groups - linear mixed effects model

I would like to fit linear mixed effects model to my dataset, but I was wondering if quantity of observations in groups matter? I have some groups with about 60 observations in each, but there are ...
2
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0answers
22 views

Calculating means and confidence intervals for groups with multiple observations in each subject using the NLME package in R

I need your help to identify group means and calculate the corresponding 95% confidence intervals for a set of independent samples with 2 dependent observations within each of those samples. I'm using ...
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0answers
14 views

How can I perform a post hoc test of a three-way interaction in SPSS using the 'test' command?

I will describe the design of the experiment briefly. The task have two within-subject variables ($A$ and $B$), A. have two levels B. have three levels one repeated variable with 5 levels ('time ...
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0answers
47 views

How to test signicance of differences between factor levels in mixed model using lme4 in R

My basic knowledge on model simplification comes from Crawley (2007) and I have also looked in Zuur et al Mixed Effects Models and Extensions in Ecology with R to try to answer my question. In ...
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1answer
35 views

Calcualting R2 in mixed models useing Nakagawa & Schielzeth's (2013) R2glmm method.

I have been reading about calculating R2 values in mixed models and after reading the R-sig FAQ, other posts on this forum (I would link a few but I don't have enough reputation) and several other ...
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0answers
34 views

Multiple covariates for each fixed effect

I'm analyzing data from a classical intervention design. Subjects were divided into groups, undertaking different interventions. Each subject was measured using the same tests before and after the ...
0
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0answers
26 views

Robust estimation in SPSS generalized mixed models

I'm using mixed models in SPSS 19 to analyse dietary data. The mixed procedure is used because we have more than one measurement from many of the participants. My problem is that many of my dependent ...
0
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2answers
33 views

Convergence errors in parametric bootstraps (PBmodcomp) of lmer models

I am using PBmodcomp from the pbkrtest to perform a parametric bootstrap model comparison. However, for some of the comparisons a warning message stating that the models failed to converge appear. A ...
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0answers
16 views

Design of Experiments

Help me point out the differences between mixed-effects model, subsampling model and split-plot model. Thank you very much in advance!
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0answers
9 views

analysing multiple individuals in specific time points for similarities

I am looking for a suitable analysis to examine my data for the presence of foraging individuals at different time periods, and whether the individual are in the same place over time. My dataset is ...
0
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1answer
71 views

mixed effects model output

lets say we have this: model2 <- lmer(milk.amount~(1|cow), data=milk, REML=FALSE) model1 <- lmer(milk.amount~(1|cow), data=milk) summary(model2) Linear mixed model ...
2
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0answers
38 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 ...