similar to: Problems installing mice package

> On Nov 15, 2017, at 1:08 AM, Jeremie Juste <jeremiejuste at gmail.com> wrote: > > > > Hello, > > I tried intalling mice package and got the following error: > > * installing *source* package ?mice? ... > ** package ?mice? successfully unpacked and MD5 sums checked > ** libs > g++ -I/usr/local/lib/R/include -DNDEBUG

Hello all, I am in the process of trying to install Shiny Server on a DreamHost VPS. As Shiny Server must be built on Debian, I've been muddling my way through the steps but I have run into a roadblock w/ httpuv. Following these <https://github.com/rstudio/httpuv> instructions, I ran the devtools::install_github('httpuv', 'rstudio') command and received the error pasted

Sorry, I was not clear enough. The reason I want to use mice::mice() rather than library(mice); mice() is that I want to call it from my own package. But the reprex works from the command line as well, straight after launching R: mice::mice(airquality) #> Error in check.method(setup, data): The following functions were not found: mice.impute.pmm, mice.impute.pmm The mice.impute functions

I want to call the mice function from the MICE package from my own package. However I run into this issue, which can be reproduced on the command line: mice::mice(airquality)#> Error in check.method(setup, data): The following functions were not found: mice.impute.pmm, mice.impute.pmm I have no problems when doing library(mice) mice(airquality) Is this a bug or am I missing something?

Dear Ole One of the experts may be able to diagnose this without extra information but I suspect you have not got the right magic in your NAMESPACE file in your package. You may need to re-read section 1.5.1 of the Writing R extensions manual. Michael On 04/10/2017 13:47, Ole H?st wrote: > I want to call the mice function from the MICE package from my own package. > However I run into

IIUC, this would be an isssue with MICE (or rather "mice"), which isn't Ole's. It could be a namespace issue, but it could also be that some start-up code is not executed if library() is bypasses (see .onAttach et al.). -pd > On 4 Oct 2017, at 17:00 , Michael Dewey <lists at dewey.myzen.co.uk> wrote: > > Dear Ole > > One of the experts may be able to

Hello, It's difficult to help without a sample of the format. Can you provide a short sample like 10 lines and a few columns.? Best regards, Jeremie On Wed, Jan 10, 2018 at 6:12 PM, John Kane via R-help <r-help at r-project.org> wrote: > Have a look at http://stackoverflow.com/questions/5963269/how-to-make- > a-great-r-reproducible-example > and >

Dear R users, I am using package mice and I am getting the error " Error in if (meth[j] != "") { : argument is of length zero." I have tried using several different versions of R (even the one that will be coming out this month) to no avail. I am using RStudio as my interface with R. Also note that I had run this a couple of days ago and it was working fine; I can't,

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

I'm trying to use either mice or norm to perform multiple imputation to fill in some missing values in my data. The data has some missing values because of a chemical detection limit (so they are left censored). I'd like to use MI because I have several variables that are highly correlated. In SAS's proc MI, there is an option with which you can limit the imputed values that are

Hi, I am attempting to impute my data for missing values using the mice function. However everytime I run the function it freezes or lags. I have tried running it over night, and it still does not finish. I am working with 17000 observations across 32 variables. here is my code: imputed.data = mice(data, + m = 1, + diagnostics = F) Thank you in advance, Richard [[alternative HTML version

Hello, Have a look at the plm package https://cran.r-project.org/web/packages/plm/index.html It has a convenient way to structure your data into panel according to some id. Best regards, Jeremie On Wed, Jan 10, 2018 at 5:41 PM, deva d <devazresearch at gmail.com> wrote: > dear all, > > i need some help in structuring my data file for a hierarchical time series > analysis.

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

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 everyone, Does anyone here has experience using MICE to impute missing value? I am having problem to pool the imputed dataset for a MANOVA test, could you give me some advice please? Here is my code: > library(mice) >

Hi, I'm trying to impute a large data set using mice but I keep getting this: Error in nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, : too many (2944) weights nnet.default uses the argument MaxNWts to set a maximum number of weights. I've tried to change nnet.default to get around this, but mice is somehow still passing an argument that sets the maximum

Hello How come that the function md.pattern() from package 'mice' delivers a warning when run over data sets with more than 31 variables? library( 'mice' ) x <- as.data.frame( matrix( sample( c(1:3, 1:3, 1:3, NA), 7000, repl=TRUE ), ncol=35, dimnames=list(NULL, paste('V', 11:45, sep="") ) ) ) md.pattern(x) # Warning message:

Hi all, I am imputing missingness of  90 columns  in a  data frame using mice. But "mice" gives back :  Error in nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE,  :   too many (1100) weights Any idea to solve this error is welcome, Anera [[alternative HTML version deleted]]

Hello, I have some trouble understanding why !b &&TRUE is TRUE. Do you have an idea? > b <- matrix(c(0,1,1,0,1,0),2) > !b [,1] [,2] [,3] [1,] TRUE FALSE FALSE [2,] FALSE TRUE TRUE > !b &&TRUE [1] TRUE Best regards, Jeremie

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?