random effects model

Hi all, I've a question which regression model to use? I've the following model: Taxavoid= PC + Before/after + PC * Befor/after tax avoid = continious where PC is ratio variable of political party/ i coudl have used 1 or 0 (REPvsDEM) but, I use ratio, more info. PC= REP / (DEM+REP) so ratio...

Here is an example of three different methods (fixed effects, random effects) for analyzing Models in R Commander

Hi everyone, R question: 10 participants underwent a weight loss programme, like so: mydata<-as.data.frame(matrix(c(140,125,120,115,110,110,110,105,100,90,85,100,140,70,100,100,140,120, 220,190,90,100,120,60,90,110,130,110,120,140,NA,65,110,50,NA,90,120,NA,130,150,NA,60,NA,45...

Let U0 denotes intercept variance and U1 denotes slope variance. Given that the coverage rate for the intercept variance is 91% (U0) , and the coverage rate for the slope variance is 91.2% (U1) . Also nominal coverage rate is 95%. Then it is written that I have not understood from the...

A two-level model, with one explanatory variable at the individual level (X) and one explanatory variable at the group level (Z): Y_{ij}=\gamma_{00}+\gamma_{10}X_{ij}+\gamma_{01}Z_{j}+\gamma_{11}X_{ij}Z_{j}+u_{0j}+u_{1j}X_{ij}+e_{ij}\ldots (1) correlation between u_{0j} and u_{1j} is 0 . The...

A multilevel model, with one explanatory variable at the individual level (X) and one explanatory variable at the group level (Z): Y_{ij}=\gamma_{00}+\gamma_{10}X_{ij}+\gamma_{01}Z_{j}+\gamma_{11}X_{ij}Z_{j}+u_{0j}+u_{1j}X_{ij}+e_{ij} correlation between u_{0j} and u_{1j} is 0 . In this...

When I fit lmer model with my data , there is no warning message. But when I tried to construct confidence interval by `confint` , it shows the following warning message : Warning messages: 1: In FUN(X[[i]], ...) : non-monotonic profile 2: In nextpar(mat, cc, i, delta, lowcut...

This pdf illustrates nicely how is to test the random effect of multilevel model . But I am simulating data from a two-level model and estimating the parameters of the model for various combination of the parameters. For each condition , I generated 1000 simulated data sets. I have used `R` for...

The following model assumes that the covariance between the random intercepts and random slopes across Subjects is Zero . lmer(Reaction ~ Days + (Days||Subject), sleepstudy) How can I write the same model by "lme" function under nlme pacakge assuming zero covariance between the random...

I am trying to fit a random slope model by "varComp" in R . For the following example , that is in "lmer" syntax , how can I write it in "varComp" syntax : library(lme4) library(varComp) fm1 <- lmer(Reaction ~ Days + (Days||Subject), sleepstudy) I am not understanding...

I want to extract the "standard error" of variance component from the output of "lmer" . In Chapter 12 , Experiments with Random Factors , of the book Design and Analysis of Experiments, written by Douglas C. Montgomery , at the end of the chapter , Example 12-2 is done by SAS . In Example...

I want to extract the "standard error" of variance component from the output of "lmer" . library(lme4) model <- lmer(Reaction ~ Days + (1|Subject), sleepstudy) The following produces estimates of variance component : s2 <- VarCorr(model)$Subject[1] It is **NOT**...

Can you please explain where is the difference between the following two models : fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy) I noticed there is some discrepency in the estimate for random effect...

library(lme4) fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) The notation `(Days | Subject)` says to allow the intercept and `Days` to vary randomly for each level of `Subject` . Can you please explain me the result of the following commands ...

Attached is the output I received in Stata for my random-effects model. I have three time-invariant variables (WS, WRLURI, UA) and three time-varying variables (A_TP, A_GDP, A_E). I'm not sure how to 1) interpret the results of the regression and 2) interpret the results of the LM test for...

I am using panel data and I have to choose between fixed-effect and random-effect models. I run the Hausman test, the H0 (i.e., the difference in the coefficients from the two models is not systematic) is rejected. Thus, I should use the fixed-effect. I also run a second test, which is based...

I'm having trouble choosing which model to use to analyze my findings. My meta-analysis is about how effective a drug is compared to placebo or no intervention to reducing pain during needle procedures. So I've collected the studies I need, I'm looking at a specific drug and the outcome...

Hi, I am using a random effects model to analyse a 4 point time series longitudinal data to compare the effects of an intervention and control, some of my independent variables are non-parametrically distributed - how sensitive is random effects model to this? can i use it or should i perform a...