similar to: how to pool imputed data sets with latent class analysis and binary logistic regression

Dear R users I want to combine multiply imputed data-sets generated from mice to do classfication. However, I have various questions regarding the use of mice library. For example suppose I want to predict the class in this data.frame: data(nhanes) mydf=nhanes mydf$class="pos" mydf$class[sample(1:nrow(mydf), size=0.5*nrow(mydf))]="neg" mydf$class=factor(mydf$class) First I

My problem is I have data with both categorial and numerical data, currently only the categorical number contains missing data, was wondering do I make a new dataframe containing only the categorical columns? How would you use Latent Class Model specifically poLCA to impute the missing data? http://www.sscnet.ucla.edu/polisci/faculty/lewis/pdf/poLCA-JSS-final.pdf The reason why I chose not to

Dear all, I request your help to solve a problem I've encountered in using 'mice' for multiple imputation. I want to apply a logistic regression model. I need to extract information on the fit of the model. Is there any way to calculate a likelihood ratio or the McFadden-pseudoR2 from the results of the logistic model? I mean, as it is possible to extract pooled averaging and odds

R-helpers: I have a dataset that has 168 subjects and 12 variables. Some of the variables have missing data and I want to use the multiple imputation capabilities of the "mice" package to address the missing data. Given that mice only supports linear models and generalized linear models (via the lm.mids and glm.mids functions) and that I need to fit Cox models, I followed the previous

Hi folks, I have five imputed data sets and would like to apply the same recoding routines to each. I could do this sort of thing pretty easily in Stata using MIM, but I've decided to go cold turkey on other stats packages as a incentive for learning more about R. Most of the recoding is for nominal variables, like race, religion, urbanicity, and the like. So, for example, to recode race

Dear R users, I have three questions measuring close relationships. The questions are same and the respondents put the answer in order. I'd like to examine the pattern of answers and visualize it. For example q1 (A,B,C,D,E) and q2 and q3 are the same. If the respondents selects A B C (so BCA or BAC or CBA or CAB), I'd like to construct frequency table for ABC and other combinations for

Hi, Does anybody know how to obtain the imputed SNP genotype probabilities from the snpStats package? I am interested in using an imputation method implemented in R to be further used in a simulation study context. I have found the snpStats package that seems to contain suitable functions to do so. As far as I could find out from the package vignette examples and its help, it gives the

I am doing multiple imputation with Hmisc, and can't figure out how to replace the NA values with the imputed values. Here's a general ourline of the process: > set.seed(23) > library("mice") > library("Hmisc") > library("Design") > d <- read.table("DailyDataRaw_01.txt",header=T) > length(d);length(d[,1]) [1] 43 [1] 2666

Dear R users, I have a reproducible data and try to create new variable "clo" is 1 if know variable is equal to "very well" or "fairly well" and getalong is 4 or 5 otherwise it is 0. rep_data<- read.table(header=TRUE, text=" id1 id2 know getalong 100000016_a1 100000016_a2 very well 4 100000035_a1 100000035_a2 fairly

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

Hi r-help, I use lavaan:sem() for structural equation modelling with latent variables. Below is a reproducible example (the code requires a working installation of lavaan) where the latent variable criminality is in focus. Besides criminality in general, I am specifically interested one of the manifest variables that make up the latent variable criminality, namely fire.setting. My question is:

Hi all, I am hardly an expert, so I expect that this code is not the easiest/ most efficient way of getting where I want. Any suggestions in that direction would also be helpful. I am working on panel analysis with five imputed datasets, generated by Amelia. To do panel analysis, it seemed that the plm package was the best, providing a convenient wrapper for fixed and random effects

Hi Can anyone please suggest me a package where I can estimate a non-linear regression model? One of the independent variables is latent or unobserved. I have an indicator variable for this unobserved variable; however the relationship is known to be non-linear also. In terms of equations my problem is y=f(latent, fixed) q=g(latent) where q is the indicator variable For me both f and g are

Hello folks, I am a relatively new user of R and created multiply imputed data sets with the 'mice' library. This library provides two functions for complete-data analysis on multiply imputed data set objects (lm.mids and glm.mids). I am trying to estimate a series of Cox PH regression models and cannot figure out the best way to do this. Is it possible with the mitools library?

Hi, I'm using the library MICE to make multiple imputations. I'can pool the results to show how the predicted values fit, but how to combine the five imputed datasets? I take the mean? for exemple : x1<-complete(imp) x2<-complete(imp, 2) x3<-complete(imp, 3) x4<-complete(imp, 4) x5<-complete(imp, 5) impcomp<-(x1+x2+x3+x4+x5)/5 Or there is an other way to do this?

Hello, All, GWAF 1.2 R.Version() is below. system(lme.batch.imputed( phenfile = 'phenfile.csv', genfile = 'CARe_imputed_release.0.fhsR.gz', pedfile='pedfile.csv', phen='phen1', covar=c('covar1','covar2'), kinmat='imputed_fhs.kinship.RData', outfile='imputed.FHS.IBC.GWAF.LME.output.0.txt' )) Gives the error messages: Error in

Dear list members I am new to the list and would be much appreciated if you could help me. I am very interested in applying Latent Class Model for analysing multiple raters agreement data. I have recently found a paper by Uebersax, J. (1993) which described a method in a form of Located Latent Class Analysis (llca). Uebersax has written a Fortran program which is available on the web, for the

I want estimate MIMIC latent variable with R in a Monte Carlo simulation. The packages plspm and semPLS don't permit to introduce MIMIC variable but only reflexives or formatives variables. The only one program which permits to use MIMIC latent variable with PLSPM seems to be XLSTAT, which can not be used to simulate a lot of data bases. It is a real challenge to develop a package with

Hello I would like to pass a model formula as an argument to the with.mids function from the mice package. The with.mids functon fits models to multiply imputed data sets. Here's a simple example library(mice) #Create multiple imputations on the nhanes data contained in the mice package. imp <- mice(nahnes) #Fitting a linear model with each imputed data set the regular way works

Dear R-users, I'd like to announce the release of my new package "ltm" (available from CRAN), for fitting Latent Trait Models (including the Rasch model) under the Item Response Theory approach. The latent trait model is the analogous of the factor analysis model for Bernoulli response data. "ltm" fits the linear one- and two-factor models but also allows for