Displaying 20 results from an estimated 3000 matches similar to: "rrp.impute: for what sizes does it work?"
2003 Jul 25
1
Difficulty replacing NAs using Hmisc aregImpute and Impute
Hello R experts
I am using Hmisc aregImpute and Impute (following example on page 105 of The
Hmisc and Design Libraries).
*My end goal is to have NAs physically replaced in my dataframe. I have
read the help pages and example in above sited pdf file, but to no avail.
Here is example of what I did.
Ph, my data frame, is attached.
> xt <- aregImpute (~ q5 + q22rev02 + q28a, n.impute=10,
2005 Jan 19
1
Imputation missing observations
>From Internet I downloaded the file Hmisc.zip and used it for R package updation. and R gave the message 'Hmisc' successfull unpacked.
But when I use the functions like aregImpute the package is displaying coundn't find the function
Where as in help.search it is giving that use of the function
>
2011 Mar 31
2
fit.mult.impute() in Hmisc
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
2003 Dec 08
1
Design functions after Multiple Imputation
I am a new user of R for Windows, enthusiast about the many functions
of the Design and Hmisc libraries.
I combined the results of a Cox regression model after multiple imputation
(of missing values in some covariates).
Now I got my vector of coefficients (and of standard errors).
My question is: How could I use directly that vector to run programs such
as 'nomogram', 'calibrate',
2005 May 04
3
Imputation
I have timeseries data for some factors, and some missing values are there in those factors, I want impute those missing values without disturbing the distribution of that factor, and maintaining the correlation with other factors. Pl. suggest me some imputation methods.
I tried some functions in R like aregImpute, transcan. After the imputation I am unable to retrive the data with imputed
2011 Jun 23
2
Rms package - problems with fit.mult.impute
Hi!
Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute?
I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm.
Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof"))
One needs to specify y=T and x=T in the fit. But
2003 Jul 27
1
multiple imputation with fit.mult.impute in Hmisc
I have always avoided missing data by keeping my distance from
the real world. But I have a student who is doing a study of
real patients. We're trying to test regression models using
multiple imputation. We did the following (roughly):
f <- aregImpute(~ [list of 32 variables, separated by + signs],
n.impute=20, defaultLinear=T, data=t1)
# I read that 20 is better than the default of
2003 Jun 16
1
Hmisc multiple imputation functions
Dear all;
I am trying to use HMISC imputation function to perform multiple imputations
on my data and I keep on getting errors for the code given in the help
files.
When using "aregImpute" the error is;
>f <- aregImpute(~y + x1 + x2 + x3, n.impute=100)
Loading required package: acepack
Iteration:1 Error in .Fortran("wclosepw", as.double(w), as.double(x),
2008 Nov 26
1
multiple imputation with fit.mult.impute in Hmisc - how to replace NA with imputed value?
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
2003 Jul 28
2
aregImpute: warning message re: acepack and mace
hi,
i'm trying to learn how to use aregImpute by doing the examples provided with
the package, and after installing Hmisc.1.6-1.zip (for Windows),
and running the very first example on R 1.7.1, i get an error message warning
me about "mace" (see below) and acepack.
i found the acepack package, but its filename ends in tar.gz
and i'm finding it difficult to open (because its
2010 Nov 01
1
Error message in fit.mult.impute (Hmisc package)
Hello,
I would like to use the aregImpute and fit.mult.impute to impute missing
values for my dataset and then conduct logistic regression analyses on the
data, taking into account that we imputed values. I have no problems
imputing the values using aregImpute, but I am getting an error at the
fit.mult.impute stage.
Here is some sample code (I actually have more observations and variables to
2005 Jul 09
1
aregImpute: beginner's question
Hello R-help,
Thanks for everyone's very helpful suggestions so far. I am now trying to
use aregImpute for my missing data imputation. Here are the code and error
messages. Any suggestions would be very much appreciated.
Sincerely,
Anders Corr
########################################
#Question for R-Help on aregImpute
########################################
#DOWNLOAD DATA (61Kb)
2010 Aug 10
1
Multiple imputation, especially in rms/Hmisc packages
Hello, I have a general question about combining imputations as well as a
question specific to the rms and Hmisc packages.
The situation is multiple regression on a data set where multiple
imputation has been used to give M imputed data sets. I know how to get
the combined estimate of the covariance matrix of the estimated
coefficients (average the M covariance matrices from the individual
2005 Jul 08
2
missing data imputation
Dear R-help,
I am trying to impute missing data for the first time using R. The norm
package seems to work for me, but the missing values that it returns seem
odd at times -- for example it returns negative values for a variable that
should only be positive. Does this matter in data analysis, and/or is
there a way to limit the imputed values to be within the minimum and
maximum of the actual
2008 Jun 30
3
Is there a good package for multiple imputation of missing values in R?
I'm looking for a package that has a start-of-the-art method of
imputation of missing values in a data frame with both continuous and
factor columns.
I've found transcan() in 'Hmisc', which appears to be possibly suited
to my needs, but I haven't been able to figure out how to get a new
data frame with the imputed values replaced (I don't have Herrell's book).
Any
2003 Jun 12
3
Multiple imputation
Hi all,
I'm currently working with a dataset that has quite a few missing
values and after some investigation I figured that multiple imputation
is probably the best solution to handle the missing data in my case. I
found several references to functions in S-Plus that perform multiple
imputation (NORM, CAT, MIX, PAN). Does R have corresponding functions?
I searched the archives but was not
2010 Jun 30
3
Logistic regression with multiple imputation
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).
2005 Nov 09
2
error in NORM lib
Dear alltogether,
I experience very strange behavior of imputation of NA's with the NORM
library. I use R 2.2.0, win32.
The code is below and the same dataset was also tried with MICE and
aregImpute() from HMISC _without_ any problem.
The problem is as follows:
(1) using the whole dataset results in very strange imputations - values
far beyond the maximum of the respective column, >
2010 May 04
1
aregImpute (Hmisc package) : error in matxv(X, xcof)...
Dear r-help list,
I'm trying to use multiple imputation for my MSc thesis.
Having good exemples using the Hmisc package, I tried the aregImpute function. But with my own dataset, I have the following error :
Erreur dans matxv(X, xcof) : columns in a (51) must be <= length of b (50)
De plus : Warning message:
In f$xcoef[, 1] * f$xcenter :
la taille d'un objet plus long n'est pas
2004 Nov 30
2
impute missing values in correlated variables: transcan?
I would like to impute missing data in a set of correlated
variables (columns of a matrix). It looks like transcan() from
Hmisc is roughly what I want. It says, "transcan automatically
transforms continuous and categorical variables to have maximum
correlation with the best linear combination of the other
variables." And, "By default, transcan imputes NAs with "best