Displaying 20 results from an estimated 3000 matches similar to: "missing data codes"
2006 Sep 15
2
prediction interval for new value
Hi,
1. How do I construct 95% prediction interval for new x values, for example - x = 30000?
2. How do I construct 95% confidence interval?
my dataframe is as follows :
>dt
structure(list(y = c(26100000,
60500000, 16200000, 30700000, 70100000, 57700000, 46700000, 8600000,
10000000, 61800000, 30200000, 52200000, 71900000, 55000000, 12700000
), x = c(108000, 136000,
2006 Sep 27
1
Any hot-deck imputation packages?
Hi
I found on google that there is an implementation of hot-deck imputation in
SAS:
http://ideas.repec.org/c/boc/bocode/s366901.html
Is there anything similar in R?
Many Thanks
Eleni Rapsomaniki
2006 Nov 29
2
Dummies multiplied with other variable
Hi,
I would like to estimate something like y = a + b*d2*y + c*d3*y where
the dummies are created from some vector d with three (actually many
more) levels using factor(). But either there is included the variable
y or d1*y. How could I get rid of these?
Example:
x = c(1,2,3,4,5,6,7,8)
y = c(3,6,2,8,7,6,2,4)
d = c(1,1,1,2,3,2,3,3)
fd = factor(d)
lm(x ~ fd*y)
gives:
Coefficients:
(Intercept)
2006 Oct 19
2
How to get multiple Correlation Coefficients
Hi
I have used a polycor package for categorical correlation coefficients.
I run the following script. But there were no results.
Could you tell me how to correct the script?
Thanks in advance,
vars <- names(sdi)
for (i in 1:length(vars)) {
for (j in 1:length(vars)) {
paste(vars[i]," and ", vars[j])
polychor(vars[i], vars[j])
# corr
}
}
--
Kum-Hoe Hwang, Ph.D.Phone :
2003 Jun 12
3
Multiple imputation
Hi all,
I'm currently working with a dataset that has quite a few missing
values and after some investigation I figured that multiple imputation
is probably the best solution to handle the missing data in my case. I
found several references to functions in S-Plus that perform multiple
imputation (NORM, CAT, MIX, PAN). Does R have corresponding functions?
I searched the archives but was not
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 Jan 19
1
Imputation missing observations
>From Internet I downloaded the file Hmisc.zip and used it for R package updation. and R gave the message 'Hmisc' successfull unpacked.
But when I use the functions like aregImpute the package is displaying coundn't find the function
Where as in help.search it is giving that use of the function
>
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',
2003 Jul 28
2
aregImpute: warning message re: acepack and mace
hi,
i'm trying to learn how to use aregImpute by doing the examples provided with
the package, and after installing Hmisc.1.6-1.zip (for Windows),
and running the very first example on R 1.7.1, i get an error message warning
me about "mace" (see below) and acepack.
i found the acepack package, but its filename ends in tar.gz
and i'm finding it difficult to open (because its
2007 Jun 30
2
Standard Probability Distributions.
Um texto embutido e sem conjunto de caracteres especificado associado...
Nome: n?o dispon?vel
Url: https://stat.ethz.ch/pipermail/r-help/attachments/20070630/6d94caca/attachment.pl
2007 Jan 08
1
Multivariate OLS
Dear all R users,
Suppose I have a VECTOR of time series y[t] consists of 2000 data point. For example suppose I have data frame which has two columns. First column represents a time series of exchange rate for 2000 days. And the second column represents the price of a commodity for the same period. Now I want to fit a OLS regression like that,
y[t] = a + b*delta[y[t-1]] + c*delta[y[t-2]] +
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; summary(fit)
will use standard errors, t, P from last imputation only.
Use
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 27
3
Converting text to numbers
Hi,
I have Forecast Class and Observed Class in a data matrix as below.
> Sample1
FCT OBS
1 1 5
2 2 4
3 3- 3+
4 3 3
5 3+ 3-
6 4 2
7 5 1
I want to find the difference between Observed and Forecast Classes.
How can I get this done?
I tried to following to convert the 1 through 5 classes, to 1 through 7
for both OBS and FCT column.
> Sample1$OBS2 <- Sample1$OBS
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
2006 Sep 19
3
Reading a file in R
Dear R helpers,
I am trying to read a CSV file in R called EUROPE (originally an Excel
file which I have saved as a CSV file) using the command
EUROPEDATA <- read.csv("EUROPE.csv")
EUROPE.csv is basically a matrix of dimension 440*44, and has a line of
headers, i.e. each column has a name.
Using read.csv I can't load the data into R properly. Although the first
20 columns or
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),
2010 Jun 30
3
Logistic regression with multiple imputation
Hi,
I am a long time SPSS user but new to R, so please bear with me if my
questions seem to be too basic for you guys.
I am trying to figure out how to analyze survey data using logistic
regression with multiple imputation.
I have a survey data of about 200,000 cases and I am trying to predict the
odds ratio of a dependent variable using 6 categorical independent variables
(dummy-coded).
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,
2011 Jun 23
2
Rms package - problems with fit.mult.impute
Hi!
Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute?
I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm.
Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof"))
One needs to specify y=T and x=T in the fit. But