On Fri, 25 Jun 2004, Henrik Andersson wrote:
> The last newsletter found on www.r-project.org is from December 2003.
>
> I was looking forward to the next issue, will there be one soon, or what
> happened ?
>
Yes, there will be one very soon.
-thomas
BTW: the last issue, while titled De
On Sunday 04 July 2004 21:02, [EMAIL PROTECTED] wrote:
> All,
>
> I have some data of animal movements that I'm plotting using xyplot()
> from lattice. I want to have the date (class POSIXct object) on the
> Y-axis and the animals longitude on X-axis. Eg.
>
> xyplot(date ~ longitude, groups = anima
On Sun, 2004-07-04 at 19:41, Richard A. O'Keefe wrote:
> Last week there was a thread on outlier detection.
> I came across an article which has a very interesting paragraph.
>
> The article is
> Missing Values, Outliers, Robust Statistics, & Non-parametric Methods
> by Shaun Burke, RHM Te
All,
I have some data of animal movements that I'm plotting using xyplot() from lattice. I
want to have the date (class POSIXct object) on the Y-axis and the animals longitude
on X-axis.
Eg.
xyplot(date ~ longitude, groups = animal, data = my.data)
with data like:
animal ptt year mo
Thank you all for your quick answers.
With respect to my question on smooth noncumulative baseline cox hazard, I
followed Prof Brian Ripley and I used the following:
library(survival)
plot(basehaz(coxfinal2)[,2]/365.25+1945,basehaz(coxfinal2)[,1],t="l")
xx <-
seq(min(basehaz(coxfinal2)[,2]/365.25
On 07/04/04 16:06, Ludo Max wrote:
>
>20 subjects were measured in 5 conditions (thus repeated measures) and
>for each subject in each condition there are 4 response measures (thus
>multivariate as it is a combined score that needs to be compared across
>the conditions).
>
>So, using a multivariate
Last week there was a thread on outlier detection.
I came across an article which has a very interesting paragraph.
The article is
Missing Values, Outliers, Robust Statistics, & Non-parametric Methods
by Shaun Burke, RHM Techology Ltd, High Wycombe, Buckinghamshire, UK.
It was the fourth a
I see a case where "f1" gives the wrong answer:
b <- array(c("a:b", "a", "c", "b:c"), dim=c(2,2))
a <- b[c(1,1),]
For these two matrices, f1(a,b) == c(2,2), while f2(a,b) ==
c(2,0). If b does not contain ":", e.g., if it is numeric, then this
pathology can not occur. Howev
Thanks to Gabor, Marc, and Spencer for their elegant solutions. Gabor's first
solution worked the best for me.
Best,
Ravi.
From: [EMAIL PROTECTED] on behalf of Gabor Grothendieck
Sent: Sat 7/3/2004 12:12 PM
To: [EMAIL PROTECTED]
Subject: Re: [R] counting the o
> Dear Sirs,
>
> I used the nlme package for the statistical analysis of my data for
> prepared MS. Please, can you write me what is your recommended form of
> citation of this program?
> Thanks in advance for your reply.
>
I "cheat" and cite the book that supports it:
@Book{Pinheiro:2000:MMS,
Prof Brian Ripley <[EMAIL PROTECTED]> writes:
> > I'm doing the same job as Hegre et al. (studying civil wars) but with the
> > counting process formulation of the Cox model. (I use intervals, my formula
> > looks like Surv(start,stop,status)~ etc.).
>
> Careful, that is left- and right- censore
If you have a smooth cumulative hazard you can differentiate it to get a
differentiable hazard (using smooth in its technical sense).
Try
http://www.stats.ox.ac.uk/pub/MASS4/VR4stat.pdf
for possible approaches in S/R. There are several, e.g. in packages
muhaz, polspline, sm, locfit, Wh
20 subjects were measured in 5 conditions (thus repeated measures) and
for each subject in each condition there are 4 response measures (thus
multivariate as it is a combined score that needs to be compared across
the conditions).
So, using a multivariate approach to repeated measures this is a
I'm trying to read John Chambers (1998) Programming with Data
(Springer), and I'm finding many functions (e.g., deparseText on p. 102
or levelsIndex on p. 203) that seem not to be available on R 1.9.1;
many of these are available in S-Plus 6.2, though often with limited
documentation. I
Hi everyone.
There's been several threads on baseline hazard in Cox model but I think
they were all on cumulative baseline hazard,
for instance
http://tolstoy.newcastle.edu.au/R/help/01a/0464.html
http://tolstoy.newcastle.edu.au/R/help/01a/0436.html
"basehaz" in package survival seems to do a cum
Many thanks to Diethelm for the new Rmetrics release 191.10057, and to the
CRAN masters for including it in the archive.
I have updated the initial packages that had been prepared for and included
in Quantian 0.5.9.2, and just completed uploading them to Debian's incoming/
directory. As brand-new
You can do this:
f <- function(x)x
fix(f)
# you are now put into an editor and can change the function
# when you save the file you will be taken back to R with the new version of f
Alternately, you can ease the use of the source style of operating
by hitting up-arrow a few times in the R consol
It is a pleasure for me to announce the new built for Rmetrics Version
191.0057. The source files and Windows binary packages can be downloaded
from www.rmetrics.org .
The new built has also been submitted to the CRAN server. Some new
functions
and example files have been added. Unfortunately the
On Sun, 4 Jul 2004, Jiacheng Yuan wrote:
> I am using the windows version R1.9.0.
> I used to be a Splus user. When I used Splus to try some computation, I like
> to put all my codes in a script file and check them line by line. This way I
> can keep track of all my thinking and it's very easy t
Please do your homework. qda is part of MASS (uncredited by you) and the
posting guide does ask you to consult that book. If you had done so you
would have found examples (start with the one on p.340)
On Sat, 3 Jul 2004, Robin Gruna wrote:
> I'm a R newbie and I have a supervised 2-class class
Hi there,
I am using the windows version R1.9.0.
I used to be a Splus user. When I used Splus to try some computation, I like
to put all my codes in a script file and check them line by line. This way I
can keep track of all my thinking and it's very easy to make correction at
some earlier steps
Peter Mathe <[EMAIL PROTECTED]> writes:
> I recently upgraded to Suse 9.1 for Amd64.
> So far I could not find precompiled binaries of R-1.9.1 for this case.
> So I tried installation from source, but could not succeed. Although
> the configuration/installation procedure ran without problems, the
I recently upgraded to Suse 9.1 for Amd64.
So far I could not find precompiled binaries of R-1.9.1 for this case.
So I tried installation from source, but could not succeed. Although the
configuration/installation procedure ran without problems, the make
check always ended with errors. When tryin
On Sun, 4 Jul 2004, Ajay Shah wrote:
> > It might clarify your thinking to note that a seasonal ARIMA model
> > is just an ``ordinary'' ARIMA model with some coefficients
> > constrained to be 0 in an efficient way. E.g. a seasonal AR(1) s =
> > 4 model is the same as an ordinary (nonseasonal) A
Thank you for the very prompt response. I only included a small part of the
output to make the message brief. I'm sorry it did not provide enough detail to
answer my question. I have appended the summary() and anova() outputs to the
two models I fitted in R.
Quoting Prof Brian Ripley <[EMAIL PROTE
> It might clarify your thinking to note that a seasonal ARIMA model
> is just an ``ordinary'' ARIMA model with some coefficients
> constrained to be 0 in an efficient way. E.g. a seasonal AR(1) s =
> 4 model is the same as an ordinary (nonseasonal) AR(4) model with
> coefficients theta_1, theta_
Looking at the significance of a main effect (group) in the presence of an
interaction (time:group) is hard to interpret, and in your case is I think
not even interesting. (The `main effect' probably represents difference
in intercept for the time effect, that is the group difference at the last
t
Dear list-members
I am new to R and a statistics beginner. I really like the ease with which I can
extract and manipulate data in R, and would like to use it primarily. I've
been learning by checking analyses that have already been run in SAS.
In an experiment with Y being a response variable,
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