Mixed (aka multilevel or hierarchical) models are linear models that include both fixed effects and random effects. They are used to model longitudinal or nested data.

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Simulation of random-effects meta-analysis yields biased tau^2

I need to realistically simulate study effect sizes and within-study variances for a random-effects meta-analysis in which the outcome is a relative risk. My question: why does this simulation lead to ...
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mixed noise and gaussian

I have a large number of data sets. Each data set has something 200K data points lying in a square times a circle. The square is solid $I\times I$. The circle $S^1$ is hollow (dim 1). By reasoning ...
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Nested random factor with confounding (random?) variable

I have a question on how to specify a GLMM. I made an experiment with two treatments (control and treated) to test the effect of a water contaminant on reproductive cells of tadpoles. I have data on ...
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Cumulative link model with categorical or continuous predictors?

I have data in a perception experiment where I show surfaces slanted at different angles and ask participants to judge whether the surface could be stood on. I also asked them how certain they are ...
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Analysis of variability for independent variables

I have dataset of four urinary markers collected over a period of 10 years.All these markers are independent of each other. The hypothesis is there is no difference in values for each marker collected ...
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I want to check the “affect of economic variables on the export of vegetable ” [on hold]

I have used ARDL technique for that as I applied that technique following answer come to me. Value of Durbin Watson test (2.20). Please guide me how i can interpret that value?? In the long run ...
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1answer
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How to include nested fixed effects with different levels across conditions in lmer?

My design is as follows: 1. one dependent variable (brain activity), 2. a "condition" factor I manipulated with two levels (c1 and c2) 3. a "region of interest" factor with two levels (r1 and r2) *...
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Variance of Linear Combination of Fixed Effects in Mixed ANOVA Model

Consider the mixed effects model described here. It suffices to consider only the balanced case. I need to make inference about the linear combinations of the fixed effects $L = \sum_i c_i \tau_i$ I ...
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Estimate of Fixed Effect in Mixed Effects ANOVA (Restricted versus Unrestricted)

Consider the mixed effects ANOVA model described here It is stated that the estimate of the fixed effect is $\hat{\tau}_i = \bar{y}_{i..}-\bar{y}_{...}$. But is this true for both the unrestricted ...
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14 views

Confidence intervals for odds ratio in GLMM with interactions

I have the following output from a GLMM The model is $$ \log\left(\dfrac{p_{i}}{1 - p_{i}}\right) = \beta_0 + \beta_1 \texttt{type}_i + \beta_2\texttt{treat}_i + \beta_3(\texttt{type}\times\texttt{...
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How to model GEE (generalized estimating equation) in data coming from two datasets?

I would like to model X (sentiment score, continuous between -1 and 1) and Y (smoking status, either 0 or 1). Individuals can be clustered by the "State" variable. It would be the most ideal if I ...
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1answer
45 views

Unbalanced linear mixed effect modeling for longitudinal data with lme4

I'm new to longitudinal analyses, and I'm having trouble formulating a model that accurately reflects my study design. This study recruited subjects for two groups (dx vs. control), with measurements ...
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Hierarchical regression model with just 2 populations

I have a dataset with the scores in Mathematics of students coming from 2 different schools. I'm trying to implement a hierarchical linear model to predict the student score given a set of covariates. ...
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29 views

What could be the reason for a coefficient's change in magnitute and/or sign for identical specifications of OLS and Random Intercept Models?

What could be the reason(s) for a coefficient's change in magnitute and/or sign for identical specifications of OLS and Random Intercept Models? Further, does the Random Intercept Model, controlling ...
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35 views

Should I use a mixed-effects model?. Measures at different locations in several pieces

Imagine a simple experiment: I have 10 "almost identical" pieces. I take the first one and I measure the temperature at 5 different distances from its center. I take the second one and I do the same ...
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1answer
20 views

Fitting and interpreting random effects in repeated measures and unbalanced ecological data set

I have a vegetation data set that consists of 150 plots that were sampled 1-3 times over a three year period. Plots are my unit of observation and they are unbalanced (since plots were sampled either ...
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Multilevel meta-analysis with non-independent effect sizes: correct model?

I'm conducting a meta-analysis on standardised mean difference scores. Some studies provide multiple effect sizes, thereby violating the assumption of independence. An example is given below (all ...
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28 views

Finding differences between groups with linear mixed model

I am trying to analyse how a measured variable differs between groups and time. My data has such structure: ...
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Use of Weights in Linear Mixed Models to improve error structure instead of transforming the Dependent Variable

I am runnning a Random Coefficient Mixed Model in R using lme in {lme4}. I had to transform ...
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Model design and nonconvergence problem with GLMM, incomplete block design in R

I have a two-part question that includes issues with generalized linear mixed models and failure to converge. First, a little bit about my experimental design. I have data where I am trying to test ...
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4 views

Variance component analysis on marginally penalized LMEs

I have a series of nested LME's, each tier of which has been marginally penalized using elastic net (lassop function in {MMS} R). I'm very interested in looking at shifts in variance component ...
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1answer
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permutation testing and mixed effects models

I am rather new to both permutation tests and mixed effects models, so forgive me if this is a ridiculous question. I would like to run a permutation test for a model that has a random effect, ...
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Formula for variance of estimated variance components for experimental design

Consider a simple linear mixed model with one random effect. $Y_{ij}=\mu+\alpha_i+e_{ij}$ where $\alpha_i\sim N(0,\sigma^2_\alpha)$ for $i=1,...,I$, and $e_{ij}\sim N(0,\sigma^2_e)$ and $cov(\alpha,e)=...
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Heteroskedasticity and Distribution of the Dependent Variable in Linear Models

I am running a multivariate ols model where my dependent variable is Food Consumption Score, an index created by the weighted sum of the consumption occurrences of some given food categories. ...
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Inconsistent pattern between mixed regression and data plot

I built a mixed linear regression model which includes a dependent variable 'dv', independent variable 'v1' & 'v2', and subject ID 'subject'. The R syntax is shown below: output <-lmer(dv ~ ...
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49 views

model for non-linear data with repeated measurements

I have to do a model for non-linear data with repeated measurements. I worked with predatory insects. I did an experiment with 4 treatments, where per each treatment predators received a different ...
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1answer
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Can I compare lme models with and without quadratic (polynomial) term?

I am running mixed-effects analysis using lme package in R. I am trying to understand whether a linear model or a polynomial curve would better capture the change in a variable over time. I know ...
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25 views

Beta values for mixed models

First of all, I am very new to statistics, so I apologize if this is a fairly obvious question. I am using R to run mixed models to include in a paper, and my professor has requested the "results" of ...
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Variance structure with GLMMPQL

I want to know the influence of different climate variables on the abundance of berries. I have an average number of berries per plot and a number of plot per site (unbalanced design) for a number of ...
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1answer
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R: anova() vs. Anova() for test of categorical predictor from glmer or glm.nb object

In R, I'm wondering how the functions anova() (stats package) and Anova() (...
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Algorithm for mixed-effects models with 100 random effects

I am wondering if there is any algorithm can estimate a mixed-effects model with 100 random effects, i.e., the covariance matrix $\boldsymbol D$ for random effects is 100$\times$100. I tried the ...
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Is it possible to do mediational analysis on trial-level data, including random subject and items factors?

I have words that are rated on several different dimensions. Each of say 30 subjects rates a set of say 30 words. I am curious if some feature of the words is related to some rated dimension of the ...
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Conditional Logit Model with varying Choice set

I am struggling with the project I am doing now. For each observation in my dataset, we draw a random radius circle and locate alternatives in the circle as the observation's choice set. Then all the ...
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43 views

Scaling after Principal Component Analysis

I am attempting to model the yield of various crops as a function of weather data, namely one temperature variable and 7 moisture-related variables (measuring different aspects of moisture content). ...
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Quantifying changes in the functional form of a response curve

I've inherited observations of a response variable ($y$) measured over time ($t$) during which the response increases and subsequently decreases. The measurement of this response occurs repeatedly ...
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34 views

Difference between car and lmerTest results for lmer model [lme4]

I fit a mixed effects model using lme4 and compared the anova tables generated by the packages "lmerTest" and "car". Both should be able to handle lme4 objects. When running the below code, the ...
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Permutation mixed effects and post-hoc

I measured gonad diameters monthly for the same individuals (repeated measures). However, many months are missing some individuals. Various months were measured over different years, though some ...
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Calculation of standard errors and confidence intervals in lme (nlme package) and jointModel (JM) package

Does anyone know how the standard errors are calculated for mixed models fit using "nlme" package and joint models fit using "JM" package? I am trying to compare the precision between mixed models fit ...
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Parametric bootstrap testing for random effect in GLMM

I am trying to learn GLMM using R. From what I have understood parametric bootstrap is a robust method to know the significance of fixed as well as random effects ...
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Using lme (or lmer) building a mixed model with repeated measures, a covariate, and multiple blocking factors (treatment and patch within treatment)

Our study is looking at annual plant Biomass (ANPP) across several sites and years. Each site has four ...
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How to handle nesting in a mixed effects model?

I am working on a dataset that has two groups, control and treatment. The data were collected from participants of each group at two time points. At each time point, there were multiple observation ...
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Derivation of variance function for mixed-effects negative binomial

In the Stata 13/Stata 14 manuals, the variance function for a two-level mixed-effect negative binomial is given as: Var(yij)=[1+{exp(sigma^2)(1+alpha)-1}E(yij)]E(yij) I have been trying to figure ...
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Academic Reference Request on Multivariate Mixed Model

The problem at hand was to predict two values, $Y^1$ and $Y^2$, from another one, $X$. We decided that a mixed model would be apropriate considering the clustered nature of data. After visual ...
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Add extra level to multilevel model that was not part of the sampling process

Consider a population of students, clustered within schools. We are interested on explaining results of a math test at the student level. Assume we use a multistage sampling process in order to ...
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Mixed Model To Fit Exponential Decay Process “Within Subjects”, then regress the half-life on a between-subjects factor

Here is my problem: I have a set of $N = 3000$ reactions following first order kinetics, and I have 6 timepoints taken at 3 minute intervals for each of these $N$ reactions, where I am measuring my ...
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what other models are used to predict system resource needed to server increase in volume for an application

I am trying to use models to predict underlying cpu or memory needed to serve an application volume. For example, I have a data set like this: ...
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Interpreting a mixed-effects meta-analysis using the metafor package in R

This relates to an earlier question. Mixed effects meta-analysis using metafor package in R It is very easy to perform a mixed-effects meta-analysis e.g. using ...
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Random effects not appearing for some levels in lmer model - Why would that be?

Here's my code in R but I unfortunately can't share my data, and I can't reproduce it by randomly creating a data set. I can say that the data set has 1,561 cases....
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How to analyse this hierarchical-like data better?

I have a dataset whose structure is as below. Here f1, f2, f3 etc are a set of features. There are differennt models to predict each Vi from all fjs. There are different models to predict each Wi ...