Hello,
this is not really an R-related question, but since 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
I am trying to read a number of XML files using xmlTreeParse(). Unfortunately,
some of them are malformed in a way that makes R crash. The problem is that
closing tags are sometimes repeated like this:
tagvalue1/tagtagvalue2/tagsome garbage/tag/tagtagvalue3/tag
I want to preprocess the contents
Hello everyone,
I use latex() (Hmisc) for report generation and thus have been affected by
the problem with rounding decimals described, for example, in this post:
http://thread.gmane.org/gmane.comp.lang.r.general/73287/focus=73287
In short, numbers often are printed with 15 or so decimals even
Hello,
and sorry if this is already explained somewhere. I couldn't find anything.
R (2.3.1, Windows) seems to perform some kind of lazy evaluation when
evaluating defaults in function calls that, at least for me, leads to
unexpected results. Consider the following, seemingly equivalent
http://cran.r-project.org/bin/windows/base/rw-FAQ.html#What_0027s-the-best-way-to-upgrade_003f
[EMAIL PROTECTED] wrote:
Hello everyone.
Currently I am running R 2.2.1 (windows), and I will like to update to
2.3. I wanted to ask if it is possible to update without having to removed
2.2.1;
Question 2: Try saving the data as an SPSS portable file. I never had
trouble reading these in R.
Finn Sandø wrote:
When I try to import an spss sav file with read.spss() I am getting the
following error
'Error in read.spss(X:\\.sav) : error reading system-file header'
and the import
Like the error message tells you, cor does not have an argument na.rm.
use=complete already does what you want, namely casewise deletion of
missing values.
However, this will not work with vectors of unequal length (how is R to
determine which observations in x correspond to those in y?). What
Hello and thank you for your answers, Andrew and Berwin. If I'm not
mistaken, the mixed-model version of Berwin's approach would be:
#My stuff:
DF-data.frame(x1=rep(c(0,1),each=50),x2=rep(c(0,1),50))
tmp-rnorm(100)
DF$y1-tmp+DF$x1*.5+DF$x2*.3+rnorm(100,0,.5)
Hello,
suppose I have a multivariate multiple regression model such as the
following:
DF-data.frame(x1=rep(c(0,1),each=50),x2=rep(c(0,1),50))
tmp-rnorm(100)
DF$y1-tmp+DF$x1*.5+DF$x2*.3+rnorm(100,0,.5)
DF$y2-tmp+DF$x1*.5+DF$x2*.7+rnorm(100,0,.5)
x.mlm-lm(cbind(y1,y2)~x1+x2,data=DF)
fix(data)
will invoke edit(data) and store changes you make in data without
displaying anything.
stat stat schrieb:
Dear all R users,
I have a query on Edit function. Suppose I have a data frame named
data. I can use EDIT function to see the materials contained in data, by
using
I am not sure I understand what you want to do, but maybe some of this
will be helpful. I first generate some data that should resemble yours:
dat-expand.grid(Region=1:3, Species=1:4, Sex=c(M,F))
dat-do.call(rbind,lapply(1:10,function(x) dat))
dat$Bodysize-rnorm(nrow(dat),10,2)
Now what the
Hello,
Working on a little primer to ease the transition from SPSS to R, I have
encountered two problems with write.foreign.
One is cosmetic (but still annoying): the text data files will contain
NA for missing values. Each time SPSS encounters an NA, it will print
a warning. This could be
Vielen Dank, Spencer.
I could not find a published example where both the original data and
conditional posterior variances were available. Instead, I toyed around
a little with artificial data, and the (pretty pathetic) result below is
the closest I came to Monte Carlo-ing. I'm afraid I lack
Hello,
I'm sorry to resurrect this thread that I started almost two months ago.
I've been pretty busy since I posted my question and the issue is not
that high on my priority list. Thanks to all those who replied, and I
hope I can tickle your interest again.
As a reminder, my question was how
Hello,
I am looking for a way to obtain standard errors for emprirical Bayes estimates
of a model fitted with lmer (like the ones plotted on page 14 of the document
available at
http://www.eric.ed.gov/ERICDocs/data/ericdocs2/content_storage_01/000b/80/2b/b3/94.pdf).
Harold Doran mentioned
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