Displaying 20 results from an estimated 1000 matches similar to: "multiple imputation manually"
2010 Dec 29
0
Simulating data and imputation
Hi,
I wrote a script in order to simulate data, which I will use for evaluating
missing data and imputation. However, I'm having trouble with the last part
of my script, in which a dataframe is constructed without missing values.
This is my script:
y1 <- rnorm(10,0,3)
y2 <- rnorm(10,3,3)
y3 <- rnorm(10,3,3)
y4 <- rnorm(10,6,3)
y <- c(y1,y2,y3,y4)
a1 <-rep(1,20)
a2
2009 Apr 23
2
Two 3D cones in one graph
Dear R-users:
The following code produces two cones in two panels. What I would like
to have is to have them in one, and to meet in the origin. Does anyone
have any good ideas how to do this?
Thanks for your help
Jaakko
library(lattice)
A<-matrix(ncol=2, nrow=64)
for(i in 0:63)
{
A[i+1,1]<-sin(i/10)
A[i+1,2]<-cos(i/10)
}
2010 Aug 30
1
New to R
I'm relatively new to R, and not particularly adept yet, but I was wondering
if there was a simply way to simulate missing data that are MAR, MNAR and
MCAR. I've got a good work-around for the MCAR data, but it's sort of hard
to work with.
Josh
[[alternative HTML version deleted]]
2005 Nov 14
1
Little's Chi Square test for MCAR?
Hi.
Can anyone point me to any module in R which implements "Little's Chi
Square test" for MCAR.
The problem is that i have around 60 behavioural variables on a 6 point
categorical scale which i need to test for MCAR and MAR. What i can make
out from preliminary analysis is that moderate (0.30 to 0.60)
correlations may be present in several variable pairs leading me to
suspect
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
2012 Aug 13
1
R-help question
Hi there,
I have subscribed to R-help but am not sure how to view or post questions? I think this is the right way.
I am planning on doing a multivariate regression investigating the relationship between depression (a continuous variable) and social support variables (mostly continuous, some categorical) among older people. I have a number of demographic and health-related variables that I am
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
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
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
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 Oct 10
1
Multiple imputation on subgroups
Dear R-users,
I want to multiple impute missing scores, but only for a few subgroups in my
data (variable 'subgroups': only impute for subgroups 2 and 3).
Does anyone knows how to do this in MICE?
This is my script for the multiple imputation:
imp <- mice(data, m=20, predictorMatrix=pred, post=post,
method=c("", "", "", "",
2004 Mar 15
2
imputation of sub-threshold values
Is there a good way in R to impute values which exist,
but are less than the detection level for an assay?
Thanks,
Jonathan Williams
OPTIMA
Radcliffe Infirmary
Woodstock Road
OXFORD OX2 6HE
Tel +1865 (2)24356
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),
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 0 and 1, shows 3 different
values after imputation (0, 1 and 2).
I
2005 May 04
3
Imputation
I have timeseries 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
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, “Error: subscript out of
bounds.” Can anyone tell if my commands
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
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
- MLE
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