search for: mitool

R 2.2.0 windows XP I am beginning to explore the mitools package contributed by Thomas Lumley (thank you Thomas) and I have a few questions: (1) In the examples given in the mitools documentation, the only family argument used is family=binomial. Does the package support family=gaussian and other link functions? I ran the with function with family=gau...

Hey there I am estimating a multilevel model using lmer. I have 5 imputed datasets so I am using mitools to pool the estimates from the 5 > > datasets. Everything seems to work until I try to use > MIcombine to produced pooled estimates. Does anyone have any suggestions? The betas and the standard errors were extracted with no problem so everything seems to work smoothly up until that po...

R-users & helpers: I am using Amelia, mitools and cmprsk to fit cumulative incidence curves to multiply imputed datasets. The error message that I get "Error in eval(expr, envir, enclos) : invalid 'envir' argument" occurs when I try to fit models to the 50 imputed datasets using the "with.imputationList" functi...

Hi all, I am trying to use the functions in the plm package with multiply imputed datasets. I had tried to combine the datasets using the imputationList() function of mitools. plm, however, requires a data argument, and I don't know where to point it to. I'd appreciate any help people might have. A (possible) fuller description of the problem and code is in a previous message: https://stat.ethz.ch/pipermail/r-help/2007-August/ 138670.html. Thanks. -N...

What does missInfo compute and how is it computed? There is only 1 observation missing the ethnic3 variable. There is no other missing data. N=1409 > summary(MIcombine(mod1)) Multiple imputation results: with(rt.imp, glm(G1 ~ stdage + female + as.factor(ethnic3) + u, family = binomial())) MIcombine.default(mod1) results se (lower upper)

R-helpers: I am using R 2.5 on Windows XP, packages all up to date. I have run into an issue with the MIcombine function of the mitools package that I hoped some of you might be able to help with. I will work through a reproducible example to demonstrate the issue. First, make a dataset from the pbc dataset in the survival package --------------- # Make a dataset library(survival) d <- pbc[,c('time','status',...

...ts 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? If so, it would be great if somebody could forward me some code or provide a simple example of this? Any comments or suggestions would be appreciated. Regards, Brian

...a problem when the covariate analysis is added. We think the problem is with the R code to run the covariate data analysis. We have the control stream, R code (along with the error), and data attached. If anyone can help we would really appreciate it. Thank you very much. Michael R Code: # load MItools from within R library(MItools) ProjectDir <- "~/EssentialsOpenCourseware/continuous_PKPD/Examples" NMcom <- "nm6osxg77big.pl" cov <- c("AGE", "BW") # run NONMEM using 3032.ctl NONR(ProjectDir=ProjectDir, NMcom=NMcom, dvname="Test...

...set1$RACE, '3=1; else=0; ') miset1$hispanic <- recode(miset1$RACE, '4=1; else=0; ') miset1$raceother <- recode(miset1$RACE, '5=1; else=0; ') I've tried a number of variations, e.g., on the following using recode (from the car package) with imputationList (from the mitools package), though without success: files.allmisets <- list.files(getwd(),pattern="miset*.csv$",full=TRUE) allmis <- imputationList(lapply(files.allmisets, read.csv)) allmis <- update(allmis, white <- recode(RACE, '1=1; else=0; ')) I've also tried some basic loops...

Hi, I am a long time SPSS user but new to R, so please bear with me if my questions seem to be too basic for you guys. I am trying to figure out how to analyze survey data using logistic regression with multiple imputation. I have a survey data of about 200,000 cases and I am trying to predict the odds ratio of a dependent variable using 6 categorical independent variables (dummy-coded).

...'fts' 'its' 'timeDate' 'timeSeries' 'tis' 'tseries' 'xts' 'foreach' 'TSA' 'RSQLite' 'tkrplot' 'sgeostat' 'mapproj' 'tcltk2' 'R2wd' 'png' 'tree' 'VIM' 'mitools' 'Zelig' 'HSAUR' 'mvtnorm' 'lme4' 'robustbase' 'mboost' 'coin' 'xtable' 'sandwich' 'coxme' 'zoo' 'strucchange' 'dynlm' 'biglm' 'chron' 'acepack' 'TeachingDemos...

Dear All, I am doing a growth modeling using NLME. I have three levels in my data: observation, individual, household. About half of my total sample have missing values in my household-level covariates. Under this situation, the best way to go is probably to multiply impute the data (for, say, 5 times), estimate the same model separately on each model using LME function, and merge the results. My

...t;country", time="year") pdata.frame(data2, "country", time="year") pdata.frame(data3, "country", time="year") pdata.frame(data4, "country", time="year") pdata.frame(data5, "country", time="year") #Using mitools, combine the panel data frames 1-5 allhiv <- imputationList(list(data1,data2,data3,data4,data5)) #A test regression form2<-APIPolSupport~subnatexpoftotal+adultprev.lag+pressfreedom #The following is something I tried but which didn't work. estimateHIV1 <-with(allhiv, plm(form2, data...

Hi, I have a big dataset which has many missing values and want to implement Multiple imputation via Monte carlo markov chain by following J Schafer's "Analysis of incomplete multivariate data". I don't know where to begin and is looking for a sample R code that implements multiple imputation with EM, MCMC, etc.... Any help / suggestion will be greatly appreciated. David

Hi I have used the amelia command from the Amelia R package. this gives me a number of imputed datasets. This may be a silly question, but i am not a statistician, but I am not sure how to combine these results to obtain the imputed dataset to usse for further statistical analysis. I have looked through the amelia and zelig manuals but still can not find the answer. This maybe because I dont

Dear R-Users, The following question is more of general nature than a merely technical one. Nevertheless I hope someone get me some answers. I have been using the mice package to perform the multiple imputations. So far, everything works fine with the standard regressions analysis. However, I am wondering, if it is theoretically correct to perform nonparametric models (GAM, spline

Hello all, if I have multiple imputed data sets, is there a command or function in R in any package you know of to combine those, I know one common MI approach is rubins rules, is there a way to do this using his rules or others? I know theres ways, like using Amelia from Gary King's website to create the imputed data sets, but how to make them into one or combine them for analysis.

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use

Hello: I am working with a stratified survey dataset with sampling weights and I want to use multiple imputation to help with missingness. 1. Is there a way to run an ordered logistic regression using both a multiply imputed dataset (i.e. from mice) and adjust for the survey characteristics using the weight variable? The Zelig package is able to do binary logistic regressions for survey

...urvey package is now on CRAN. Since the last announcement (version 3.6-11, about a year ago) the main changes are - Database-backed survey objects: the data can live in a SQLite (or other DBI-compatible) database and be loaded as needed. - Ordinal logistic regression - Support for the 'mitools' package and multiply-imputed data - Conditioning plots, transparent scatterplots, survival and CDF plots. There is more information on the package web page at http://faculty.washington.edu/tlumley/survey/ -thomas Thomas Lumley Assoc. Professor, Biostatistics tlumley at u.washington.e...