Displaying 20 results from an estimated 7000 matches similar to: "Imputations in R"
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
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),
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',
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
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).
2002 Apr 08
4
Missing data and Imputation
Hi Folks,
I'm currently looking at missing data/imputation
methods (including multiple imputation).
S-Plus has a "missing data library".
What similar resources are available within R?
Or does one roll one's own?
Best wishes to all,
Ted.
--------------------------------------------------------------------
E-Mail: (Ted Harding) <Ted.Harding at nessie.mcc.ac.uk>
2009 Apr 24
1
Multiple Imputation in mice/norm
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
2002 May 14
2
R CMD check
I am unclear on whether to run R CMD check pgkname as user or as root on Linux. When running as user, after all the latex, html, and man files are created, I get the following error message:
Rdconv(): Couldn't open '': Permission denied
Has anyone dealt with that message?
When I run R CMD check a second time, all latex, html, etc. are recreated which takes quite a while. Is there
2009 Apr 22
1
Multiple imputations : wicked dataset ? Wicked computers ? Am I cursed ? (or stupid ?)
Dear list,
I'd like to use multiple imputations to try and save a somewhat badly
mangled dataset (lousy data collection, worse than lousy monitoring, you
know that drill... especially when I am consulted for the first time
about one year *after* data collection).
My dataset has 231 observations of 53 variables, of which only a very
few has no missing data. Most variables have 5-10% of
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, >
2012 Jun 03
1
Multiple imputation, multinomial response & random effects
Dear R-group,
Could somebody recommend a package that can deal with a multinomial response variable (choice of breeding tactic in mice, which has four unordered levels), multiply-imputed data (generated using the Amelia package) and two non-nested random effects: individual identity (133 individuals made up to four choices each) and year (for which there are six levels and sample size varies
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
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
2012 Dec 08
1
imputation in mice
Hello! If I understand this listserve correctly, I can email this address
to get help when I am struggling with code. If this is inaccurate, please
let me know, and I will unsubscribe.
I have been struggling with the same error message for a while, and I can't
seem to get past it.
Here is the issue:
I am using a data set that uses -1:-9 to indicate various kinds of missing
data. I changed
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
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 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
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
2011 Aug 01
1
Impact of multiple imputation on correlations
Dear all,
I have been attempting to use multiple imputation (MI) to handle missing data in my study. I use the mice package in R for this. The deeper I get into this process, the more I realize I first need to understand some basic concepts which I hope you can help me with.
For example, let us consider two arbitrary variables in my study that have the following missingness pattern:
Variable 1
2013 Feb 14
2
Plotting survival curves after multiple imputation
I am working with some survival data with missing values.
I am using the mice package to do multiple imputation.
I have found code in this thread which handles pooling of the MI results:
https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html
Now I would like to plot a survival curve using the pooled results.
Here is a reproducible example:
require(survival)
require(mice)
set.seed(2)
dt