Displaying 20 results from an estimated 25 matches for "transcan".
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
guess" expected val...
2007 Sep 26
1
using transcan for imputation, categorical variable
Dear all,
I am using transcan to impute missing values (single imputation). I have
several dichotomous variables in my dataset, but when I try to impute
the missings sometimes values are imputed that were originally not in
the dataset. So, a variable with 2 values (severe weight loss or
no/limited weight loss) for example coded...
2005 Jan 11
1
transcan() from Hmisc package for imputing data
Hello:
I have been trying to impute missing values of a data
frame which has both numerical and categorical values
using the function transcan() with little luck.
Would you be able to give me a simple example where a
data frame is fed to transcan and it spits out a new
data frame with the NA values filled up?
Or is there any other function that i could use?
Thank you
avneet
=====
I believe in equality for everyone, except reporters an...
2006 Mar 24
0
Imputing NAs using transcan(); impute()
Dear all,
I'm trying to impute NAs by conditional medians using transcan() in
conjunction with impute.transcan().
... see and run attached example..
Everything works fine, however impute() returns saying
Under WINDOWS
> x.imputed <- impute(trans)
Fehler in assign(nam, v, where = where.out) :
unbenutzte(s) Argument(e) (where ...)
Zus?tzlich: Warnmeldung:...
2003 Jun 16
1
Hmisc multiple imputation functions
...uot;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),
as.double(runif(lw)), :
C/Fortran function name not in load table
When I use "transcan" it is;
>f <- transcan(~y + x1 + x2, n.impute=10, shrink=FALSE)
Error in transcan(~y + x1 + x2, n.impute = 10, shrink = FALSE) :
Must specify data= when using R
I am not sure what I am missing.
Vumani
2012 Jul 05
0
Confused about multiple imputation with rms or Hmisc packages
...ervations. I'm not a statistician, but based on my readings of Frank Harrell's book it seems to be a candidate for using multiple imputation technique(s). I understand the concepts behind imputation, but using the functions in rms and Hmisc is confounding me. For instance, whether to use transcan or aregImpute.
Here is a sample of my data: https://dl.dropbox.com/u/1852742/sample.csv
Drawing from Chapter 8 of Harrell's book, this is what I've been toying with:
#recurfree_survival_fromsx is survival time, rf_obs_sx codes for events as a binary variable.
#The CPH model I would lik...
2005 May 04
3
Imputation
...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 values. Please give me some way to get the data with imputed values.
Thanks in advance
Yours truely
Ramesh Kolluru
[[alternative HTML version deleted]]
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 pointers would be appreciated.
================================================...
2004 Aug 14
0
Re: extracting datasets from aregImpute objects
...at has been useful. However, the help file
possibly suggests that I should get a flat-file matrix of n.impute
imputations, presumably with indexing. I'm a bit stuck using
alternatives to aregImpute, as neither MICE nor Amelia seem to like my
dataset, and Frank Harrell no longer recommends Transcan for multiple
imputations.
-----
David,
aregImpute produces a list containing the multiple imputations:
w <- aregImpute(. . .)
w$imputed$blood.pressure # gets m by k matrix
# m = number of subjects with blood pressure missing,
# k = number of multiple imputations
To get a completed da...
2007 Jun 22
1
Imputing missing values in time series
...11/16/2006 6.907
What I would like to do is to fill the NAs with the price from the
previous date * gas used during holidays is purchased from the week
before. Though real simple, I wonder if there is a function to perform
this task. Some of the imputation functions I'm aware of (eg. impute,
transcan in Hmisc) seem to deal with completely different problems.
2.5.0/Windows XP
Thanks in advance.
HT
2005 Jul 09
1
aregImpute: beginner's question
...propdemocracy+avetrade1984dollars+concentration+cycle+polarity+propmid+terrgainer+
demgainer+ fedgainer+ popdengainer+ urbpopgainer+ tradeopgainer+
gdppcgainer+ terrloser+ demloser+ fedloser+ popdenloser+ urbpoploser+
tradeoploser+ gdppcloser, lm, f,
+ data=d)
Error in impute.transcan(xtrans, imputation = i, data = data, list.out =
TRUE, :
inconsistant naming of observations led to differing length
vectors
>
2002 Aug 04
5
Pseudo R^2 for logit - really naive question
I am using GLM to calculate logit models based on cross-sectional data. I
am now down to the hard work of making the results intelligible to very
average readers. Is there any way to calculate a psuedo analoque to the R^2
in standard linear regression for use as a purely descriptive statistic of
goodness of fit? Most of the readers of my report will be vaguely familiar
and more comfortable with
2004 Aug 13
0
Re: extracting datasets from aregImpute objects
..., that has been useful. However, the help file possibly suggests that I should get a flat-file matrix of n.impute imputations, presumably with indexing. I'm a bit stuck using alternatives to aregImpute, as neither MICE nor Amelia seem to like my dataset, and Frank Harrell no longer recommends Transcan for multiple imputations.
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2009 Mar 06
0
impcat='tree'
...;)] <- 'MI' # Combines
last 2 levels and uses a new name, MI
pf.coded <- as.integer(pf) # Save original pf, re-code to 1-4
levels(pf) <- c(levels(pf)[1:3],levels(pf)[3]) # Combine last 2 levels
of original
This is where I have the problem. I am writing an imputation rule:
w <- transcan(~sz+sg+ap+sbp+dbp+age+wt+hg+ekg+pf+bm+hx,imputed=TRUE,data=prostate,impcat='tree')
However I get the following error message(s)
Convergence criterion:1.511 0.787 0.41 0.215 0.115 0.062 Error: could
not find function "tree"
In addition: Warning messages:
1: In approx(y, x, xout =...
2009 Mar 12
1
alternative to EMV?
I need a package that can compute missing values of n-dimensional vectors for n > 2. This is a kind of interpolation, complicated in dimensions higher than 2. The idea is that I have a set of fully specified vectors (i.e., with no missing values) and I get a new vector that has one or more missing attributes; I need to fill in the missing values with values that are, based on the 'training
2004 Jun 15
1
fit.mult.impute and quantile regression
I have a largish dataset (1025) with around .15 of the data missing at random overall, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter'
2005 Apr 24
2
missing values
Hello,
I have climatic data of various years with many missing values. I would like
to know what tools in R are most suited to estimate this missing values.
(New in R and quite new on statistics).
Thanks,
G
How to use PC1 of PCA and dim1 of MCA as a predictor in logistic regression model for data reduction
2011 Aug 17
4
How to use PC1 of PCA and dim1 of MCA as a predictor in logistic regression model for data reduction
...uous variables and 9 binary variables). Using subject
matter knowledge, I selected 4 important variables. Regarding the rest 9
variables, I tried to perform data reduction by principal component
analysis (PCA). However, 8 of 9 variables were binary and only one
continuous. I transformed the data by transcan of rms package and did
PCA with princomp. PC1 explained only 20% of the variance. Still, I used
the PC1 as a predictor of the logistic model and obtained some results.
Then, I tried multiple correspondence analysis (MCA). The only one
continuous variable was age. I transformed "age" vari...
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
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