similar to: mice - undefined columns selected

Hello everyone!I have a data set with missing observations that I am trying to impute. I am using MICE and I would like the imputed values to all be positive. I have two types of variables: prices (P1 to P136) and quantities (Q1 to Q136) and I also want the range of these two types to be different. Besides these variables. I am using the squeeze function but I am unable to set it such that I

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

Dear list, Is there any one use MICE package deal with multilevel missing values here? I have a question about the 2lonly.pmm() and 2lonly.norm(), I get the following error quite often. Here is the code the error, could you give me some advice please? Am I using it in the right way? > ini=mice(bhrm,maxit=0) > pred=ini$pred > pred V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15

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

Saludo gente, antes que nada gracias por la ayuda que puedan aportarme, soy iniciante en R, estoy usando el paquete Mice para realizar imputaciones múltiples sobre variables en su mayoría categóricas. El problema está que cuando expresó este comando imp <- mice(dataset,method="polr",maxit=1) donde el dataset es un data.frame me tirá este error : iter imp variable 1 1 pial1a

Hi all, How can I calculate the mean from several imputed data sets with the package mice? I know you can estimate regression parameters with, for example, lm and subsequently pool those parameters to get a point estimate using functions included in mice. But if I want to calculate the mean value of a variable over my multiple imputed data sets with fit <- with(data=imp, expr=mean(y)) and

Saludo gente, antes que nada gracias por la ayuda que puedan aportarme, soy iniciante en R, estoy usando el paquete Mice para realizar imputaciones múltiples sobre variables en su mayoría categóricas. El problema está que cuando expresó este comando imp <- mice(dataset,method="polr",maxit=1) donde el dataset es un data.frame me tirá este error : iter imp variable 1 1 pial1a

R 2.7.2 PPC Mac OS X 10.4.11 library mice 1.13.1 I try to use mice for multivariate data imputation. My variables are numeric, factors, count data, ordered factors. First I created a vector for the methods to use with each variable ImpMethMice<-c(rep("logreg", 62), rep("polyreg",1), rep("norm",12), rep("polyreg",12)) next step was

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 I am unable to impute using the MICE command in R when imputing a binary variable using linear discriminant analysis. To illustrate my problem I have created a dataset, which consists of 1 continuous and 1 binary variable. The continuous variable is complete and the binary variable is partially observed. I am able to impute using the MICE command where the imputation methods is logistic

Hi, I am trying to use mice() with an aov() model.I can fun mice() fine but when I try to pool the results it doesn't work. For example, I can run the following: fit=with(data=imp,exp=aov(Y~B*P*T + Error(S/(B*P*T)))) pool(fit) # doesn't work Where B, P, and T are within subject (S) factors. However, I can't go the next step to pool these results as I am guessing this hasn't been

Dear list, I have a question about imputing 2 level data in MICE, could you give me some suggestions please? Thank you very much. The data set contains 35634 cases and 1007 variables, 280 of them are categorical variables, and the rest of them are continuous variables. On the second level, there are 198 units. I am trying to impute missing values for 270 categorical variables by using the

Hi all, I have a question about multiple imputation within the MICE package. I want to use passive imputation for my variable called X, because it is calculated out of multiple variables, namely Y, Z. Let's give an example with BMI. I know, that if I want to use passive imputation for BMI, I can use the following command: meth["BMI"] <- "~I(weight/(height/100)^2)"

Hello. I have run the command imp<-mice(mydata, im=c("","pmm","logreg","logreg"),m=5)  for a variable with no missing data, a numeric one and two variables with binary data. I got the following message: There were 37 warnings (use warnings() to see them) > warnings() Warning messages: 1: In any(predictorMatrix[j, ]) ... : coercing argument of

Amalia, No obtengo tus resultados. Corrí tus formulas y datos y el resultado es x <- structure(list(ï..psraid = c(202517L, 202518L, 202520L, 202523L, + 202527L, 202537L, 202543L, 202544L, 202551L, 202566L, 202570L, + 202571L, 202606L, 202619L, 202624L, 202629L, 202631L, 202632L, + 202633L, 202648L, 202657L, 202663L, 202676L, 202683L, 202685L, + 202706L, 202708L, 202709L, 202710L, 202734L,

Hi I am trying to impute missing values for my data.frame. As I intend to use the complete data for prediction I am currently measuring the success of an imputation method by its resulting classification error in my training data. I have tried several approaches to replace missing values: - mean/median substitution - substitution by a value selected from the observed values of a variable - MLE

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

Hello, I'm a graduate student in Genetics, who has just started working with R. I have been trying to do a k-means clustering of an expression data compilation, which has lots of NA values in it. As suggested in a couple of earlier posts, I tried using na.omit() and the MICE imputation algorithm to take care of the NA, but they dont seem to work that well. na.omit() deletes the entries,

I have fitted a proportional odds model, but would like to compare it to a continuation ratio model. However, I am unable to fit the CR model _including_ imputated data. I guess my troubles start with settuping the data for the CR model. Any hint is appreciated! Christian library(Hmisc) library(rms) library(mice) ## simulating data (taken from rms::residuals.lrm) set.seed(1) n <- 400 age

Dear R-users, Version V2.0 of the package mice is now available on CRAN for Windows, Linux and Apple users. Multivariate Imputation by Chained Equations (MICE) is the name of software for imputing incomplete multivariate data by Fully Conditional Specifcation (FCS). MICE V1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. MICE V1.0 introduced predictor selection,