Displaying 20 results from an estimated 188 matches for "imput".

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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")) rm(.Random.seed)
khan.imputed <- impute.kn...

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...

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 5.
# defaultLinear makes sense for our data.
fmp <- fit.mult.impute(Y ~ X1 + X2 ... [for the model of...

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; summa...

2011 Dec 02

2

Imputing data

So I have a very big matrix of about 900 by 400 and there are a couple of NA
in the list. I have used the following functions to impute the missing data
data(pc)
pc.na<-pc
pc.roughfix <- na.roughfix(pc.na)
pc.narf <- randomForest(pc.na, na.action=na.roughfix)
yet it does not replace the NA in the list. Presently I want to replace the
NA with maybe the mean of the rows or columns or some type of correlation.
Any help...

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
-...

2013 Jan 14

0

Changing MaxNWts with the mi() function (error message)

Hello,
I am trying to impute data with the mi() function (mi package) and
keep receiving an error message. When imputing the variable, "sex,"
the mi() function accesses the mi.categorical() function, which then
accesses the nnet() function. I then receive the following error
message (preceded by my code below):
&g...

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 th...

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.
> x...

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)...

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(&qu...

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
time-varying covariate as well as a response variable,
I received an error message, â€œError: subscript out of
bo...

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 rep...

2004 Sep 01

3

Imputing missing values

...ination of levels.
Price Crop Season
10 Rice Summer
12 Rice Summer
NA Rice Summer
8 Rice Winter
9 Wheat Summer
Price[is.na(Price)] gives me the missing values, and
by(Price, list(Crop, Season), mean, na.rm = T) the
values I want to impute. What I've not been able to
figure out, by looking at by and the various
incarnations of apply, is how to do the actual
substitution.
Any help would be much appreciated.
Jan Smit

2011 Dec 13

0

snpStats imputed SNP probabilities

Hi,
Does anybody know how to obtain the imputed SNP genotype probabilities from the snpStats package?
I am interested in using an imputation method implemented in R to be further used in a simulation study context.
I have found the snpStats package that seems to contain suitable functions to do so.
As far as I could find out from the pack...

2011 Jan 31

2

Rubin's rules of multiple imputation

Hello all, if I have multiple imputed data sets, is there a command or
function in R in any package you know of to combine those, I know one common
MI approach is rubins rules, is there a way to do this using his rules or
others? I know theres ways, like using Amelia from Gary King's website to
create the imputed data sets, but h...

2010 Jul 14

1

Changing model parameters in the mi package

I am trying to use the mi package to impute data, but am running into
problems with the functions it calls.
For instance, I am trying to impute a categorical variable called
"min.func." The mi() function calls the mi.categorical() function to
deal with this variable, which in turn calls the nnet.default()
function, and p...

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 ha...

2007 Jul 17

0

Multiple imputation with plausible values already in the data

...the posting guide does not
forbid asking non-R questions (even encourages it to some degree), I though I'd
give it a try.
I am currently doing some secondary analyses of the PISA (http://pisa.oecd.org)
student data. I would like to treat missing values properly, that is using
multiple imputation (with the mix package). But I am not sure how to do the
imputation, since the data set provided by the OECD already contains variables
with plausible values.
Roughly, the situation is like this: for each of the cognitive (achievement)
scales, there are five variables holding plausible values....

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 and I h...