Displaying 20 results from an estimated 1000 matches similar to: "Rubin's rules of multiple imputation"
2011 Oct 18
1
getting basic descriptive stats off multiple imputation data
Hi, all,
I'm running multiple imputation to handle missing data and I'm running into a problem. I can generate the MI data sets in both amelia and the mi package (they look fine), but I can't figure out how to get pooled results. The examples from the mi package, zelig, etc., all seem to go right to something like a regression, though all I want are the mean and SE for all the
2012 Oct 30
1
Amelia imputation  column grouping
Hi everybody,
I am quite new to data imputation, but I would like to use the R package '
Amelia II: A Program for Missing Data '. However, its unclear to me how
the input for amelia should look like:
I have a data frame consisting of numerous coulmns, which represent
different experimental conditions, whereby each column has 3 replicates. I
want amelia to perform an imputation across
2013 Jan 07
1
Amelia algorithm
Dear all.
First of all, my english isn't verry good, but I hope I can convey my concern.
I've a general question about the Amelia algorithm. I'm no mathematician or
statistician, but I had to use R and impute and analyse some data, and Amelia
showed results that fitted my expectations. I'll have to defend my choice soon,
but I haven't totally grasped what Amelia does. I'm
2005 Feb 28
1
Using mutiply imputed data in NLME
Dear All,
I am doing a growth modeling using NLME. I have three levels in my
data: observation, individual, household. About half of my total
sample have missing values in my householdlevel covariates. Under
this situation, the best way to go is probably to multiply impute the
data (for, say, 5 times), estimate the same model separately on each
model using LME function, and merge the results. My
2007 Mar 02
1
Mitools and lmer
Hey there
I am estimating a multilevel model using lmer. I have 5 imputed datasets so
I am using mitools to pool the estimates from the 5
>
> datasets. Everything seems to work until I try to use
> MIcombine to produced pooled estimates. Does anyone have any suggestions? The betas and the standard errors were extracted with no problem so everything seems to work smoothly up until
2011 Mar 31
2
fit.mult.impute() in Hmisc
I tried multiple imputation with aregImpute() and
fit.mult.impute() in Hmisc 3.83 (June 2010) and R2.12.1.
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
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
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
2011 Jul 20
1
Calculating mean from wit mice (multiple imputation)
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
2006 Sep 25
2
Multiple imputation using mice with "mean"
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

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/rhelp/2007May/132180.html
Now I would like to plot a survival curve using the pooled results.
Here is a reproducible example:
require(survival)
2005 May 26
1
PAN: Need Help for Multiple Imputation Package
Hello all. I am trying to run PAN, multilevel
multiple imputation program, in R to impute missing
data in a longitudinal dataset. I could successfully
run the multiple imputation when I only imputed one
variable. However, when I tried to impute a
timevarying covariate as well as a response variable,
I received an error message, “Error: subscript out of
bounds.” Can anyone tell if my commands
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 510% of
2008 Oct 29
1
Help with impute.knn
ear all,
This is my first time using this listserv and I am seeking help from the
expert. OK, here is my question, I am trying to use impute.knn function
in impute library and when I tested the sample code, I got the error as
followingt:
Here is the sample code:
library(impute)
data(khanmiss)
khan.expr < khanmiss[1, (1:2)]
## ## First example
## if(exists(".Random.seed"))
2004 Sep 01
3
Imputing missing values
Dear all,
Apologies for this beginner's question. I have a
variable Price, which is associated with factors
Season and Crop, each of which have several levels.
The Price variable contains missing values (NA), which
I want to substitute by the mean of the remaining
(nonNA) Price values of the same SeasonCrop
combination of levels.
Price Crop Season
10 Rice Summer
12
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 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,
2007 Sep 24
0
longitudinal imputation with PAN
Hello all,
I am working on a longitudinal study of children in the UK and trying the PAN package for imputation of missing data, since it fulfils the critical criteria of taking into account individual subject trend over time as well as population trend over time. In order to validate the procedure I have started by deleting some known values ?we have 6 annual measures of height on 300 children
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),
2018 May 24
4
Manipulation of data.frame into an array
Hello everyone,
I want to transform a data.frame into an array (lets call it mydata), where: mydata[[1]] is the first imputed dataset...and for each mydata[[d]], the first p columns are covariates X, and the last one is the outcome Y.
Lets assume a simple data.frame:
Imputed = data.frame( X1 = c(1,2,1,2,1,2,1,2, 1,2,1,2,1,2,1,2),
X2 =