similar to: problems using mice()

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,

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

Muchas gracias, pero claro en una muestra de 50 datos se ejecuta, en la muestra original de 1000 registros me tira error :( 2013/10/30 daniel <daniel319@gmail.com> > 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,

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

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

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

Dear R-help list, I am working on an optimization with R by evaluating a likelihood function that contains lots of Gamma calculations (BGNBD: Hardie Fader Lee 2005 Management Science). Since I am forced to implement lower bounds for the four parameters included in the model, I chose the optim() function mith L-BFGS-B as method. But the likelihood often returns inf-values which L-BFGS-B

Dear R-users, I want to multiple impute missing scores, but only for a few subgroups in my data (variable 'subgroups': only impute for subgroups 2 and 3). Does anyone knows how to do this in MICE? This is my script for the multiple imputation: imp <- mice(data, m=20, predictorMatrix=pred, post=post, method=c("", "", "", "",

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

Dear all, I have a huge problem after downloading and exporting data from Reuters3000 XTra: I downloaded many many monthly, quarterly and yearly data. I do not know why, but after saving, I have mixed-data sets, i.e. files which can be imported as “read.csv” and others that are in the format of “read.csv2”. Sure I could change them, but normally it should be possible to mix them… For

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)"

Take a look at package mgcv. Hope this helps. --Matt -----Original Message----- From: RenE J.V. Bertin [mailto:rjvbertin at despammed.com] Sent: Thursday, February 27, 2003 1:39 PM To: r-help at stat.math.ethz.ch Subject: [R] multidimensional function fitting Hello, I have been looking around for how to perform a multidimensional, arbitrary function fit (in any case non-linear; more below),

Dear helpers, I am trying to use the mda package downloaded from the R website, but the data set has missing values so I got an error message. Should I manually handle these missing values? I was trying to read the documents to specify any option related to missing values, but I did not find it. Please forgive me if I ignore something obvious. Thanks, Zhu Wang Statistical Science Department

Dear list, I tried to apply the logistic regression to different response variables from a dataframe and would like to store the results using the function logistic.display from the "epicalc" package in a list, but got an error message "Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1". All the response variables have value of 0 or 1. It worked

I really like these Trellis graphics but how do I get this code to skip the missing? logreg<-read.csv("logreg.csv", header=TRUE, sep=",", na.string=" ") attach(logreg) bwplot(yesno~bc_pcb_tot |varlist, data=logreg, main="Box Cox PCB transformation", auto.key=TRUE, fontfamily = "HersheySans" ) Dean Sonneborn M.S. Public Health Sciences *

sorry to bother everyone. i have question to get the coefficient and confidence interval in Logic Regression with Logistic model. below i list the R code X <- matrix(as.numeric(runif(400) < 0.5), 50,8) colnames(X) <- paste("X", 1:ncol(X), sep="") rownames(X) <- paste("case", 1:nrow(X), sep="") # Define expected result: Y = (NOT X1) AND X5 Y

I have been trying to run a logistic regression using a number of studies. Below is the syntax, error message & data. Any advice regarding what I am doing wrong or solutions are appreciated, regards Bob Green > logreg <- read.csv("c:\\logregtest.csv",header=T) > attach(logreg) > names(logreg) [1] "medyear" "where" "who"

Hi there: recently i try to use LogicReg package for a tree model(logistics fit ) . i list my code and error below: > dim(model.dat) [1] 48000 745 > fit1 <- logreg(resp = model.dat[,745], bin=model.dat[, 9:700], type = 3, select = 3, ntrees = c(1,2), nleaves=c(1,7), ) Insufficient declaration LGCn1MAX in logreg() is 20000 LGCn1MAX should be at least 48000 Please fix and

Hi, I was trying to plot the logistic regression from a regression "logreg" I just ran. I downloaded the "car" package from the R website and went to Packages -> install package from local zip file I checked in my library file and the package is there. I restarted R. I then ran the command: reg.line(logreg,col=palette()[2], lwd=2, lty=1) And I get the error: Error: could

I want to use the grplasso package on a data set where I want to fit a linear model.? My interest is in identifying significant?beta coefficients.? The documentation is a bit cryptic so I'd appreciate some help. ? I know this is a strategy for large numbers of variables but consider a simple case for pedagogical puposes.? Say I have?two 3 category predictors (2 dummies each), a binary