Dear all,
When fitting an ols.model, the confidence interval at 95% doesn't cover
the plotted data points because it is very narrow.
Does this mean that the model is 'overfitted' or is there a specific amount
of serial correlation in the residuals?
Which R functions can be used to evaluate
Jan Verbesselt wrote:
Dear all,
When fitting an ols.model, the confidence interval at 95% doesn't cover
the plotted data points because it is very narrow.
Does this mean that the model is 'overfitted' or is there a specific amount
of serial correlation in the residuals?
Which R
Hi,
i need to test the equality of proportions in a paired case, like here:
after before
+ -
+ 10 14
- 5 53
So usually i use the mcnemar.test(stats).
Due to mortality, I now got the problem that my sample sizes in both
factors are not equal any
In 'sink' help page (R version 2.2.0), you can read, in details:
If 'file' is a connection if will be opened if necessary.
Maybe:
If 'file' is a connection it will be opened if necessary.
Antonio, Fabio Di Narzo.
[[alternative HTML version deleted]]
Hello R-users!
My (simple?) doubt: How to reverse the sequence of axis Y ??
the diagram below illustrate my idea...
(default)
|
|
. |
. |
. |
3 |
2 |
1 |
0 +
0 1 2 3 ...
like I want...
0 |
1 |
2 |
3 |
. |
. |
. |
|
Hello Klebyn,
It might be that a better solution exists, but here is something that
might help you. (I used that all y's are positive!)
R maxy=ceiling(max(y))
R plot(x,maxy-y,axes=FALSE)
R axis(1)
R axis(2,0:maxy,seq(maxy,0,-1))
If not all your y's are positive define `miny' and take then
R
Jan,
It sounds like you are interested in the prediction
interval (actually band). Take a look at rather nice
exposition in Chapter 9 (pdf) of Helsel and Hirsch. It
can be downloaded at the following USGS page:
http://pubs.usgs.gov/twri/twri4a3/
Regards,
Michael Grant
--- Jan Verbesselt
The key here is to set the 'ylim' argument so that the range of the y
axis is rev()ersed.
So:
# Set 'x'
x - 0:10
# set xlim and ylim
# set ylim to rev() the range() of 'x'
plot(x, xlim = c(0, 10), ylim = rev(range(x)))
If you actually want to have the 'x' axis value of 0 in the lower
The daisy function is _very_ good!
I have been able to use it for nominal variables as well, simply by:
daisy(input)*ncol(input)
Now, for very large number of rows (say 5000), daisy works for about 3
minutes using the swap space. I probably need more RAM (only 512 on my
computer). But at least I
On 12/15/05, Roel de Jong [EMAIL PROTECTED] wrote:
Dear R-users,
because lme(r) glmmpql, which are based on Penalized Quasi Likelihood,
are not very robust with Bernoulli responses,
The current version of lmer takes method = PQL (the default) or
Laplace or AGQ although AGQ is not available
I'm maximising a reasonably complex function using nlme (version
3.1-65, have also tried 3.1-66) and am having trouble with fixed
parameter estimates slightly away from the maximum of the log
likelihood. I have profiled the log likelihood and it is a parabola
but with sum dips.
Hi Dieter,
The nearest I got to was to be able to colour row. Take a look at Table
10 here:
http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatReport/latexFineControl.pdf
I used the latex package colotbl (see the definition of \shadeRow in
section 1). I didn't play any further with this. It
I will herein outline the basics of Kalman forecasting. I have never
used sspir, and I can't find all the hooks now to produce a forecast.
Perhaps with this outline, you will be able to figure it out yourself,
possibly by reading the code for some of the sspir functions.
13 matches
Mail list logo
