I tried writing a textual comparison program in R, but found that it
is too slow for my purposes. I need to make about 145 million
comparisons of the word patterns in pieces of text. I basically
compare vectors that contain count data on a multitude of words and
find ones that are similar to
On 1/5/06 11:27 AM, Achim Zeileis [EMAIL PROTECTED] wrote:
As John and myself seem to have written our replies in parallel, hence
I added some more clarifying remarks in this mail:
Note that the Anova() function, also in car, can more conveniently compute
Wald tests for certain kinds of
Thanks Z, it's coming more into focus. I don't know what would work, though
maybe it's not impossible to have a richer set of cross-references by
interest area--e.g. People interested in econometrics may wish to
examine The views help in this regard, tho something in help itself
would be
There's a bug in my version of bootcov. I'm not sure whether to report it
here or in r-bugs, because it is in a contributed package. The bug is
straightforward, so perhaps it has been reported, tho I found no reference
in a search of the archive.
Any attempt to run bootcov with both cluster and
I'm someone who from time to time comes to R to do applied stats for social
science research. I think the R language is excellent--much better than
Stata for writing complex statistical programs. I am thrilled that I can do
complex stats readily in R--sem, maximum likelihood, bootstrapping, some
I see that I need to send my bug report to the package maintainer.
Apologies for sending it to this list. There's a lot to absorb from various
online pages when reporting a bug I missed the part about sending to the
maintainer.
Peter
__
Looks like I may have found a function that addresses my needs. Bootcov in
Design handles bootstrapping from clustered data and will save the
coefficients. I'm not entirely sure it handles clusters the way I'd like,
but I'm going through the code. If it doesn't, it looks easily
re-writeable.
Are there any functions in R for running bootstraps with clustered (as
opposed to stratified) data? I can't seem to find anything obvious in boot
or Bootstrap, though I imagine boot can be manipulated to resample from
clusters. Is that what people use?
I do see some cluster bootstrap resampling
I'd like to be able to test linear hypotheses after setting up and running a
model using optim or perhaps nlm. One hypothesis I need to test are that
the average of several coefficients is less than zero, so I don't believe I
can use the likelihood ratio test.
I can't seem to find a provision
Hi Spencer Andy: Thanks for your thoughtful input! I did at one point
look at the optim() function run debug on it (wasn't aware of
browser--that's helpful!). My impression is that optim() simply calls a C
function that handles the maximization. So if I want to break out of my
likelihood
Hi Spencer: Just realized I may have misunderstood your comments about
branching--you may have been thinking about a restart. Sorry if I
misrepresented them.
See below:
On 11/3/05 11:03 AM, Spencer Graves [EMAIL PROTECTED] wrote:
Hi, Andy and Peter:
That's interesting. I still like the
Hi Spencer: Thanks for your interest! Also, the posting guide was helpful.
I think my problem might be solved if I could find a way to terminate nlm or
optim runs from within the user-given minimization function they call.
Optimization is unconstrained.
I'm essentially using normal like curves
Hi Spencer: Thanks! This gives me a number of other ways of thinking about
this problem. My one concern is that these approaches would also run into
some difficulties with how long it takes to calculate. I'm interested not
in a single value but a matrix of over 300k values that has to be
Does anyone know how -log(x) can equal 743 but -log(x+0)=Inf? That's what
the following stream of calculations suggest:
Browse[2] -log ( 1e-323+yMat2 - yMat1 * logitShape(matrix(parsList$Xs,
nrow = numXs, ncol=numOfCurves), matrix(means, nrow = numXs,
ncol=numOfCurves, byrow=TRUE),
estimates. Guess I'll find out.
Cheers, Peter
On 10/7/05 1:12 PM, Thomas Lumley [EMAIL PROTECTED] wrote:
On Fri, 7 Oct 2005, Peter Muhlberger wrote:
Does anyone know how -log(x) can equal 743 but -log(x+0)=Inf? That's what
the following stream of calculations suggest:
Browse[2] -log
I'm trying to put together an R routine to conduct unidimensional unfolding
scaling analysis using maximum likelihood. My problem is that ML
optimization will get stuck at latent scale points that are far from
optimal. The point optimizes on one of the observed variables but not
others and for
On 1/19/05 10:31 AM, Chung Chang [EMAIL PROTECTED] wrote:
Thanks for your post.
Yes, your example is indeed similar to my question.
If i means group, j means individual(subject)
Isn't 'i' individual j group?
h:indicator(0:control;1:experiment) k:repeat(if no repeat then k=1)
the the model
Thomas Jeff: Thanks again for your thoughts. The program Thomas suggests
below is elegant, but I was avoiding that because I assumed the memory
requirements and amount of time required for a large dataset would be
substantial. Of course, it depends on what's happening 'under the hood.'
Perhaps
I'm used to statistical languages, such as Stata, in which it's trivial to
pass a list of variables to a function have that function modify those
variables in the existing dataset rather than create copies of the variables
or having to replace the entire dataset to change a few variables. In R,
Hi Chung Cheng: This seems related to a problem I'm having in some data of
mine as well. I'm new to R (played w/ it some a year ago) to lme
modeling, so take this w/ a grain of salt, but here are some thoughts:
In my problem, D would be an indicator of whether a subject was in the
control
Hi, does anyone out there have a recommendation for multilevel / random
effects and longitudinal analysis?
My dream book would be something that's both accessible to a
non-statistician but rigorous (because I seem to be slowly turning into a
statistician) and ideally would use R.
Peter
Thank you to everyone who replied to my curious problem, which just got more
curious. Today I closed my copy of R, opened up a different copy of .RData
(in another directory), one that didn't have the .print problem. Worked
w/ that for a few minutes. Then closed R again restarted from the copy
I must have messed up my R environment, but don't know how or how to undo
it. The problem is this:
I paste the following into R:
test-function()
{
print(hello)
}
And I see this:
test-function()
+ {
+ .print(hello)
+ }
test()
Error in test() : couldn't find function .print
When I do
On 7/25/03 5:53 PM, Spencer Graves [EMAIL PROTECTED] wrote:
I see a period . before 'print' in your function definition. Might
this be the problem?
spencer graves
The code I paste in has no . in front of 'print' . But when the code
displays after I put it in, R puts a . in front of it. I
I'm trying to use the source command to run commands from a file. For
instance: source(do.R), where do.R is a file in the same directory in
which I am running R.
The contents of do.R are:
ls()
print(hello)
sum(y1)
mean(y1)
After source(do.R), all I see is:
source(do.R)
[1] hello
I'm
Thanks to everyone for their suggestions on getting source to print! It
seems not everyone was aware of a couple options that gets source to print
out everything. I'm now using the following command:
source(do.R, print.eval=TRUE, echo=TRUE)
__
[EMAIL
on the robustness of ML
estimation in R!
Peter
Peter Muhlberger
Visiting Professor of Political Science
Institute for the Study of Information Technology and Society (InSITeS)
Carnegie Mellon University
A newbie question: I'm trying to decide whether to run a maximum likelihood
estimation in R or Stata and am wondering if the R mle routine is reasonably
robust. I'm fairly certain that, with this data, in Stata I would get a lot
of complaints about non-concave functions and unproductive steps
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