similar to: Mixed Effects Model Power Calculations

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How can I compute a power analysis on a multi-factor within-subjects design?

Thompson's Manual to Accompany Agresti's book refers to a package named "repeated". It's not on CRAN from what I can see. I have seen rpm's for it. Where is the best place to download this package? Rick B.

Where I work a lot of people end up using Excel spreadsheets for storing data. This has limitations and maybe some less than obvious problems. I'd like to recommend a uniform way for storing and archiving data collected in the department. Most of the data could be stored in simple csv type files but it would be nice to have something that stores more information about the variables and units.

I fit the following model using glmmPQL from MASS: fit.glmmPQL <- glmmPQL(ifelse(class=="Disease",1,0)~age+x1+x2,random=~1|subject,family=binomial) summary(fit.glmmPQL) The response is paired (pairing denoted by subject), although some subjects only have one response. Also, there is a perfect positive correlation between the paired responses. x1 and x2 can and do differ within each

> rab45+ at pitt.edu wrote: >> Jerry, >> >> Here are the two R packages. >> >> Thanks. >> >> Rick > All the fortran codes compile OK so the problem is invoking the compiler > you > want. One approach which is a work around is to rename the gfortran > executable > to somthing else like gfortran-save and create a link called gfortran that

Greetings, I'm working on some analyses where I need to calculate wilcox tests for paired samples. In my current literature search I've found a few papers on sample size determination for the wilcox test notably: Sample Size Determination for Some Common Nonparametric Tests Gottfried E. Noether Journal of the American Statistical Association

I'm estimating models using lme in a for loop. Sometimes the model doesn't converge or there are other problems. This causes the evaluation to stop prematurely. I can't remember the function name that I need to use to allow the loop to continue until the end. Could someone remind me the name of the function? I've tried searching but haven't hit upon the function. Rick B.

The stat.table function in the Epi package won't do standard deviations. It didn't seem that it would be difficult to add an "sd" function to the stat.table function. Following the example for the mean, I set up a similar function for the sd (and included it as an options) but it just won't work. (I tried sending messages to the Epi mailing list after subscribing but my mail

I'm fitting a mixed effects model: fit.1 <- lme(y~x,random=~1|id,data=df) There are two different observations for each id for both x and y. When I use plot(fit.1), there is a strong increasing linear trend in the residuals versus the fitted values (with no outliers). This also happens if I use random=~x|id. Am I specifying something incorrectly? Rick B.

I'm using groupedData from nlme. I set up a groupedData data.frame with outer=~group1. When I try to plot with outer=TRUE, I get "subscript out of bounds." This happens most of the time. When it works, I get spaghetti-type plots for comparing groups. But I don't understand why it doesn't usually work. > longa.mod.1.gd <- groupedData(mod1.logit~time|

I have a dataset relating the effects of engineering works on the level of salinity in a river before and after the works. I have modelled this using linear mixed effects models to determine if the significance and level of the response to the works. I am wanting to calculate the se of difference at several points. I am not sure of how to calculate the difference and which estimate of se I

Can GLMM take formula derived from another object? foo <- glm (OVEN ~ h + h2, poisson, dataset) # ok bar <- GLMM (OVEN ~ h + h2, poisson, dataset, random = list (yr = ~1)) #error bar <- GLMM (foo$formula, poisson, dataset, random = list (yr = ~1)) #Error in foo$("formula" + yr + 1) : invalid subscript type I am using R2.1.0, lme4 0.8-2, windows xp. Below is a dataset if you

# is there a bug in the calculation of the odds ratio in fisher.test? # Nicholas Horton, nhorton at smith.edu Fri Jan 22 08:29:07 EST 2010 x1 = c(rep(0, 244), rep(1, 209)) x2 = c(rep(0, 177), rep(1, 67), rep(0, 169), rep(1, 40)) or1 = sum(x1==1&x2==1)*sum(x1==0&x2==0)/ (sum(x1==1&x2==0)*sum(x1==0&x2==1)) library(epitools) or2 = oddsratio.wald(x1, x2)$measure[2,1] or3 =

When I try to run the example from Variogram with an lme object, I get an error (although summary works): R : Copyright 2005, The R Foundation for Statistical Computing Version 2.2.1 (2005-12-20 r36812) ISBN 3-900051-07-0 ... > fm1 <- lme(weight ~ Time * Diet, BodyWeight, ~ Time | Rat) Error: couldn't find function "lme" > Variogram(fm1, form = ~ Time | Rat, nint =

Hello Everybody: I am searching for an R package for fitting Generalized Estimating Equations (GEE) with weights (i.e. Weighted Estimating Equations). From the R documentation I found "geese(geepack)" for fitting Generalized Estimating Equations. In this documentation, under the paragraph “weights” it has been written, “an optional vector of weights to be used in the fitting process.

here appears to be a bug in the foreign library. The following code used to work, but now generates an error when 'package="SAS"' is specified: ds <- read.csv("http://www.math.smith.edu/sasr/datasets/help.csv") # running foreign package version 0.8-30 library(foreign) # this works fine write.foreign(ds, "foo", "bar", package="Stata") #

Hi all, I'm new (post #1!) and I hope you'll forgive me if I'm acting like an idiot... I have been asked for some power analyses for some mixed-effects models I'm running using lmer. My studies nearly always contain mixes of repeated-measures and between-subjects predictor variables. As an example, suppose I want to see if men or women show a stronger word frequency effect. I

On Mon, Jul 2, 2018 at 7:53 PM, Ulf Volmer <u.volmer at u-v.de> wrote: > On 02.07.2018 18:23, Thomas Schweikle wrote: > >> System boots into emergency mode because it does not find any of the >> logical volumes defined, because it does not enable the LVM volume >> group. >> >> Giving "lvm", then "vgchange -a y", followed by CTRL-D

Dear R users: I am using the glm function in order to analysis data on fertility of rabbit females, the response variable are either 1, the female became pregnant, or 0, she does not. I am using the probit link function. Actually I have several measurement per female, thus it would be nice if I can include a random permanent effect of female in the model. I order to take into account for the