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

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1answer
30 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
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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 ...
0
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0answers
14 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
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0answers
14 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
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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 ...
1
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0answers
28 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
14 views

Model selection in nlme's

I can think of four ways to perform model selection nlme's: LRT, AIC, ...
2
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2answers
35 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
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0answers
25 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
17 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
49 views

Mixed models with R - convert from SAS code

I have this SAS code running a mixed model: ...
2
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0answers
24 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 ...
1
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2answers
40 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 ...
0
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0answers
28 views

Residual plot of linear mixed model: outliers, normality, transformation

Very sorry for the confusion, I will give a more detail description of my data and apologize in advance for such a long text! I set up a temperature experiment on plants and conducted repeated ...
0
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0answers
13 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 ...
0
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0answers
7 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
16 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
28 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: ...
0
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0answers
13 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, ...
2
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0answers
25 views

Linear mixed model residual plot

I had ran a linear mixed model by SPSS and then did a residual plot. The Kolmogorov-Smirnov shown significant different for some of the factors, do I need to do a transformation on my data? If so, ...
0
votes
1answer
35 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
40 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 ...
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0answers
37 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
51 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
20 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 ...
0
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0answers
12 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
37 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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0answers
23 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
31 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
26 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
votes
1answer
69 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
37 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 ...
0
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0answers
7 views

Can use an interaction between time and Time-varying-covariate in mixed model or GEE?

I have a data like this: Subjects Y Day X ID1 30 0 0 ID1 40 1 0 ID1 60 2 1 ID1 70 3 0 ID2 20 0 0 ID2 40 1 1 ID2 50 ...
19
votes
2answers
315 views

How scared should we be about convergence warnings in lme4

If we a re fitting a glmer we may get a warning that tells us the model is finding a hard time to converge...e.g. ...
1
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0answers
53 views

including interactions in a mixed linear model in R (lme)

I'm trying to test for the effect of soil moisture on transpiration rates. I have plot-level data for 18 plots in 6 different stands of trees (3 plots x 6 stands). I want to treat "stand" as a random ...
0
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0answers
19 views

Assessing the effect of related variables using lmer in R

I am trying to run a model to describe the rate of water table drawdown in the soil. The predicted variable is rate, and I am interested in the effect of two categorical variables: (1) if the water ...
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0answers
39 views

How to fit a longitudinal model with binary outcomes

I'd like to fit a longitudinal model for where multiple subjects experience binary outcomes over time. To accomplish that, I'd like to use an additive random effect for each subject and an ...
2
votes
0answers
22 views

Correlation fitted-residuals in mixed models

IN OLS linear models, fitted (predicted) and residuals scores are uncorrelated. I was under the impression that the same held true in mixed models. However, I have here an example model where fitted ...
2
votes
1answer
40 views

conducting multi-level regression on ordinal DVs with imputed data in R

Do you know of an approach/package that facilitates mixed model regression of ordinal dependent variables on multiply imputed datasets in R? Ideally, the function takes: a list of multiply imputed ...
0
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0answers
33 views

Compare LMM GLMM (generalised linear mixed model, negative binomial) by numerical measure (AIC BIC, cross validation, R² squared) for model validation

How to compare results of generalized linear mixed model (GLMM, negative binomial) with a log transformed linear mixed model (multilevel, hierarchical) . I have a data set (counts), which is nested. ...
0
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0answers
47 views

R² (squared) from a generalized linear mixed-effects models (GLMM) using a negative binomial distribution

I try to compute the marginal and conditional R² for a GLMM using a negative binomial distribution by following the procedure recommended by Nakagawa & Schielzeth (2013) . Unfortunately, the ...
1
vote
1answer
26 views

Calculated breeding values using markers using animal model in R

Animal model (frequently used in animal science and sometime in human or plants) is mixed model with: $y$ = $X$$b$ + $Z$$u$ + $e$ y is observed values for any quantitative variable, $Xb$ is fixed ...
0
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0answers
24 views

center variable separately for each factor in linear mixed model?

I am working on a relatively simple mixed model where I have two continuous predictors with an interaction term and three sites. I am treating the two predictors as fixed effects and the site as ...
0
votes
1answer
47 views

Experimental design and mixed models

I want to test effect of 3 PH on larval development. I would like to know what is the best experimental design and statistical analysis. We can only use 3 compartments of sea water, each one with a ...
1
vote
1answer
63 views

Multivariate model in lme() with independent random effect, similar to MCMCglmm

I would like to specify a multivariate model with lme with a random effect for group which is independent across variables. I found this post, which explains that ...
3
votes
1answer
77 views

Mixed effects modelling; what to do when model is over-specified?

I'm trying to use mixed-effects modelling to analyse some data. There are a number of variables that I need to specify within the model, two of which are between-participants (...
0
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0answers
13 views

Fitting a Mixed Model with Random and Repeated effects in SAS

I have want to fit a linear regression with repeated measures and random effects. The data come from clinical observations. In CT images The dependent variable is the diameter of a lymph node lesion ...