similar to: Equation for the standard error of a predicted score for a cross-classified model

All, I recently made some changes to my package and ran R CMD check, but I am getting a warning regarding my data sets. I am running R 2.6.1 I have an image (.RData) file containing five data sets in the data subdirectory and an .Rd file for each of the data sets in the man subdirectory. When I run the check I get the warning "Data sets with usage in documentation object <dataset

The first version of the package plink has been uploaded to CRAN. plink is a package for conducting unidimensional IRT scaling and chain linking for multiple groups for single-format or mixed-format common items. The package supports eight IRT models and four calibration methods. Dichotomous Models: 1PL, 2PL, 3PL Polytomous Models: -Graded response model -Partial credit model -Generalized

The first version of the package plink has been uploaded to CRAN. plink is a package for conducting unidimensional IRT scaling and chain linking for multiple groups for single-format or mixed-format common items. The package supports eight IRT models and four calibration methods. Dichotomous Models: 1PL, 2PL, 3PL Polytomous Models: -Graded response model -Partial credit model -Generalized

Hi, I am wondering if it is possible to get an estimate of standard error of the predicted posterior probability from LDA using lda() from MASS? Logistic regression using glm() would generate a standard error for predicted probability with se.fit=T argument in predict(), so would it make sense to get standard error for posterior probability from lda() and how? Another question about standard

Hello, I am looking for a way to obtain standard errors for emprirical Bayes estimates of a model fitted with lmer (like the ones plotted on page 14 of the document available at http://www.eric.ed.gov/ERICDocs/data/ericdocs2/content_storage_01/0000000b/80/2b/b3/94.pdf). Harold Doran mentioned (http://tolstoy.newcastle.edu.au/~rking/R/help/05/08/10638.html) that the posterior modes' variances

Dear List: I have a data set with over 7000 students with about 4 observations over time per student. I want to examine the within-group fits of a random sample of this group as it takes forever to compute and draw all 7000 regressions. Here is the code I have used so far. >group<-groupedData(math~year|childid, data=scores) >group.list<-lmList(group)

I am new to mmlcr and am working on a latent class mixture model attempting to identify the trajectory and number of classes that best describes my data. I am able to find model parameters such as degrees of freedom, loglikelihood, and BIC. For example, here is a cubic 3-class model I am using. mmlcr1 <- mmlcr(outer = ~ 1 | ID, components = list(list(formula = Score ~ poly(wave,3), class =

Hello. I have 5 measurement points, my dependent variable is ordinal (0 - 3), and I want to visualize my data. I'm pretty new to R. What I want is to find out whether people with different baseline covariates have different trajectories, so I want a plot with the means trajectory of my dependent variable (the individual points do not make a lot of sense in ordinal data) on each measurement

I'm trying to understand how to plot individual growth curve trajectories, with the overall mean trajectory superimposed (preferably in a slightly thicker line, maybe in black) over the individual trajectories. Using the sleepstudy data in lme4, here is the code I have so far: library(lme4) library(lattice) xyplot(Reaction ~ Days, data = sleepstudy, group = Subject, type = 'l')

Dear R-Users, I have fitted a cross-classified generalized linear mixed model using the lmer package with the following code. Mod<-lmer(y~x+(1|a)+(1|b)+ (1|c), family=binomial) In this case, only including a covariate (x) as a fixed effect. The fitted values, using fitted(mod), correspond to the raw data nicely, and the mean of the fitted values is equal to the mean of the raw data. In

Hi all, I am trying to plot the fitted trajectories for each individual from an individual growth model (fit with a linear mixed effects model in lme). How can I plot each person's trajectory in the *same* panel, along with the mean-level trajectory? Below is an image of a plot similar to what I'm trying to create (from: http://jpepsy.oxfordjournals.org/content/31/10/1002/F6.large.jpg):

I'm fitting a lme growth curve model with two predictors and their interaction as predictors. The multilevel model is nested so that level 1 is time within the individual, and level 2 is the individual. I would like to plot the mean group-level trajectories at plus and minus 1 SD from the mean of the main effects composing the interaction term. Thus, the plot should have 4 lines (mean

?kml? is an implementation of k-means for longitudinal data (or trajectories). This algorithm is able to deal with missing value and provides an easy way to re roll the algorithm several times, varying the starting conditions and/or the number of clusters looked for. KmL 1.1.1 addition: - 7 imputations methods for longitudinal data - Calculus of three qualities criterion (Calinski&Harabatz,

?kml? is an implementation of k-means for longitudinal data (or trajectories). This algorithm is able to deal with missing value and provides an easy way to re roll the algorithm several times, varying the starting conditions and/or the number of clusters looked for. KmL 1.1.1 addition: - 7 imputations methods for longitudinal data - Calculus of three qualities criterion (Calinski&Harabatz,

The Institute for Social Research (ISR) and its Statistical Consulting Service (SCS) at York University are pleased to announce our Summer Program In Data Analysis (SPIDA) for 2012. The Program runs from May 24th to June 1st, 2012.This year?s Program focuses on the theory and practice of linear models and mixed [or multilevel] models, as they are applied to hierarchical and longitudinal data.

Hi all, I appreciate the help this list has given me before. I have a question which has been perplexing me. I have been working on doing a Bayesian calculating inserting studies sequentially after using a non-informative prior to get a meta-analysis type result. I created a function using three iterations of this, my code is below. I insert prior mean and precision (I add precision manually

Hi   I am using following code to produce a xyplot for some longitudinal data. There are 2 panels. It produced all longitudinal trajectories with mean profile. But since the dataset it very large plot looks very messy. I want to show, say 10 randomly selected individual longitudinal trajectories together with mean profile for entire dataset. Could any help me to alter the following code to do

On page 165 of Mixed-Effects Models in S and S-Plus by Pinheiro and Bates there is an example of using lme() in the nlme package to fit a model with crossed random factors. The example assumes though that the data is grouped. Is it possible to use lme() to fit crossed random factors when the data is not grouped? E.g., y <- rnorm(12); a=gl(4,1,12); b=gl(3,4,12). Can I fit an additive model

Hi, I'm having issues with what I believe is a bug in the MCMCpack's MCMCmetrop1R function. I have code that basically looks like this: posterior.sampler <- function(data, prior.mu){ log.posterior <- function(theta) log.likelihood(data, theta) + log.prior(prior.mu, theta) post.samples <- MCMCmetrop1R(log.posterior, theta.init=prior.mu, burnin=100, mcmc=1000, thin=40,

Dear R useRs, i try to compute the posterior mean for the parameters omega and beta for the following posterior density. I have simulated data where i know that the true values of omega=12 and beta=0.01. With the function postMeanOmega and postMeanBeta i wanted to compute the mean values of omega and beta by numerical integration, but instead of omega=12 and beta=0.01 i get omega=11.49574 and