. However, I cannot find these cut-off
values. I use the function lda of the MASS package, although I have
also looked at some other procedures for discriminant analysis, and
equally failed.
Thanks,
Stats Wolf
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https
Dear colleagues,
Saving a plot with pdf gives a very nice result:
pdf("myplot.pdf")
par(font=1,family='serif')
plot(pressure)
dev.off()
Doing the very same with other formats (png, jpeg, tiff) gives far
worse results. Is there anything to do to make a plot in some other
format than pdf look like
ight try adding the paper option to your pdf command because it
> eliminates that huge margin on top of the output
>
> pj
>
> On Wed, May 13, 2009 at 11:47 PM, Stats Wolf wrote:
>> Dear colleagues,
>>
>> Saving a plot with pdf gives a very nice result:
>>
>> p
I am struggling with a simple repeated-measure model:
fit<-lme(trait~year * A, random = ~1|subj/year)
A being a factor with three levels. I got have the following results
for anova(fit):
numDF denDF F-value p-value
(Intercept) 1 126 2471.4720 <.0001
year 2060
am do look at means
when interpreting the results) I would like to make some kind of
multiple comparisons, if it is at all possible.
Any suggestions if I am doing anything wrong and if not, what could I
do to get these multiple comparisons?
Thank you,
Stats
Hi,
I am comparing three treatments. The data come from a longitudinal study,
and for each treatment I have only 1 case, on which the observations across
20 years are made. My main aim is to compare the three treatments, but the
effect of time is of interest as well. Which of the many R functions
eed when choosing the
optimum model for a situation that calls for mixed effects? Of course,
the example above is overly simplistic, yet such situations can occur
-- from a complex model with a couple of random terms one can finally
get to a simple fixed-effects model. Please comment.
T
s between the
subsequent levels can be, I think, modeled with polr:
fit<-polr(result~1)
summary(fit)
Could you please explain me what do the coefficients from the above
summary mean? And how I could use the t-test (eg, what are degrees of
freedom, for example? What is the exact hypothesis be
ou please explain why? Are these different
types of weights?
Many thanks in advance,
Stats Wolf
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PLEASE do read the posting guide http://www.R-project.org/posting-guide
which
WLS is just a special case"?
Best,
Stats Wolf
On Thu, Jun 24, 2010 at 12:49 PM, Joris Meys wrote:
> Isn't that exactly what you would expect when using a _generalized_
> least squares compared to a normal least squares? GLS is not the same
> as WLS.
>
> http://www.a
Indeed, they should give the same results, and hence I was worried to
see that the results were not that same. Suffice it to look at
standard errors and p-values: they do differ, and the differences are
not really that small.
Thanks,
Stats Wolf
On Thu, Jun 24, 2010 at 2:39 PM, Joris Meys
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