Displaying 20 results from an estimated 188 matches for "imputed".
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2008 Oct 29
1
Help with impute.knn
...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.knn(as.matrix(khan.expr))
## ## khan.imputed$data should now contain the imputed data matrix
x<khan.imputed$data
Here are the results:
> library(impute)
> data(khanmiss)
> khan.expr < khanmiss[1, (1:2)]
> ## ## First example
> ## if(exists(".Random....
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
imputations and add (M+1)/M times the betweenimputation covariance
matrix), and I know how to use this to get pvalues and confidence...
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
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 vcov(fit) to get the correct covariance matrix,
sqrt(diag(vcov(fit))) to get s.e."
But the standard er...
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
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 Jan 14
0
Changing MaxNWts with the mi() function (error message)
...ata 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):
> imputed.england=mi(england.pre.imputed, n.iter=6, add.noise=FALSE)
Beginning Multiple Imputation ( Mon Jan 14 13:39:49 2013 ):
Iteration 1
Chain 1 : sex
Error while imputing variable: sex , model: mi.categorical
Error in nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE,
softmax = TRUE, :
too m...
2011 Aug 01
1
Impact of multiple imputation on correlations
...ace my missing data when such large fractions are not available?
Plot 1 (http://imgur.com/KFV9y&CmV1sl) provides a scatter plot of these example variables in the original data. The correlation coefficient r = 0.34 and p = 0.016.
Q2. I notice that correlations between variables in imputed data (pooled estimates over all imputations) are much lower and less significant than the correlations in the original data. For this example, the pooled estimates for the imputed data show r = 0.11 and p = 0.22.
Since this seems to happen in all the variable combinations that I have looked at, I...
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,
2013 Feb 14
2
Plotting survival curves after multiple imputation
...now create some missing values in the data
dt < colon
dt$rx[sample(1:nrow(dt),50)] < NA
dt$sex [sample(1:nrow(dt),50)] < NA
dt$age[sample(1:nrow(dt),50)] < NA
imp <mice(dt)
fit.imp < coxph.mids(Surv(time,etype)~rx + sex + age,imp)
# Note, this function is defined below...
imputed=summary.impute(pool.impute(fit.imp))
print(imputed)
# now, how to plot a survival curve with the pooled results ?
########## begin code from linked thread above
coxph.mids < function (formula, data, ...) {
call < match.call()
if (!is.mids(data)) stop("The data must hav...
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),
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 contain any
mistakes?
First I imported SAS dataset ‘sim’ which includes a
response variable ‘MIY1’,...
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
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
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 packag...
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 how...
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 passes it a fixed parameter MaxNWts=1500. However, as
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
2007 Jul 17
0
Multiple imputation with plausible values already in the data
...e of the plausible value variables, but all of the "normal" variables. So e.g.
the first data set would include pv1math, pv1read, HISEI, and gender; while the
second would include pv2math, pv2read, HISEI, and gender. I would run mix on the
five data sets independently and end up with five imputed data sets with no
missing values.
But is this a valid approach? There would actually be two imputation runs per
data set: one for the plausible values on the achievement scales (done by the
OECD under an unknown model), and one for the other variables (done by me with
mix). The second run would us...
2007 Sep 24
0
longitudinal imputation with PAN
...n 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 have imputed the missing values using PAN and compared the imputed values to the real values I deleted  in most individuals the imputed values fit the individual trend extremely well! However, when looking at the trend over time for a handful of individuals, the imputed value was actually lower than the previo...