Andrew Robinson [EMAIL PROTECTED] wrote:
can I suggest, without offending, that you purchase and read Peter
Dalgaard's Introductory Statistics with R or Michael Crawley's
Statistics: An Introduction using R or Venables and Ripley's Modern
Applied Statistics with S or Maindonald and Braun's
On 4/25/07, Spilak,Jacqueline [Edm] [EMAIL PROTECTED] wrote:
I have a data set that I have imported (not sure if that makes a
difference) and I would like to calculate the sum of only specific
columns. I have tried
colSums(dataset, by=list(dataset$col5), dims=1) and I get an error of
unused
It looks from your tables that you have the same residual
in both programs, suggesting that the arithmetic is correct.
The terms are in a different order. Since anova() gives
sequential sums of squares (Type I), the numerical values
depend on the order. Force both programs to use the
same order
R uses treatment contrasts for factors (ie 0/1 coding) by default.
Systat is using sum (ie sum to zero) contrasts: Try this:
options(contrasts=c(contr.sum, contr.poly)
lm(maladapt~host*increase*size2)-fm
Anova(fm, type=III)
I won't discuss the dangers of types of sums of squares and different
This news item in a data mining newsletter makes various claims for a technique
called Reduced Error Logistic Regression:
http://www.kdnuggets.com/news/2007/n08/12i.html
In brief, are these (ambitious) claims justified and if so, has this technique
been implemented in R (or does anyone have
Thanks guys for the suggestions guys- I come across this problem a lot but
now I have many solutions.
Thank you,
Stephen
--- Peter Dalgaard [EMAIL PROTECTED] wrote:
Peter Dalgaard wrote:
Stephen Tucker wrote:
Dear R-helpers,
Does anyone know how to use regular expressions to
I don't know about the claims, but I do know about this:
Recent News: January 31, 2007. St. Louis, MO - Rice Analytics
applied for a U.S. patent this week on a generalized form of
Reduced Error Logistic Regression. This generalized form allows
repeated measures, multilevel, and
You are right, panel.levelplot is indeed assuming that the
boundaries are between consecutive midpoints. There is no
built in way around that; there simply isn't enough
information available to the panel function.
The cleanest solution, in principle, is to write your own
panel function
Thanks.
I think the package outliers is what I need.
Shao chunxuan
On 4/25/07, Horace Tso [EMAIL PROTECTED] wrote:
It depends on the nature of your data set. There is a package simply
called 'outliers', which has the Grubbs/Dixon/Cochran tests. There is also
the Bonferroni outlier test in
Dear R users;
I was trying to fit a nonlinear model using gnls (nlme version 3.1-80,
R 2.5.0, WinXP) and I got the following error and warning message:
Error in gnls(ht ~ a1 * hd * (1 - a2 * exp(-a3 * (dbh/dq2))), data = hdat, :
Step halving factor reduced below minimum in NLS step
In
From what I've read (which isn't much), the idea is to estimate a
utility (preference) function for discrete categories, using logistic
regression, under the assumption that the residuals of the linear
predictor of the utilities are ~ Type I Gumbel. This implies the
independence of irrelevant
Hi,
In this newsletter (Vol 7, 1),the article on AMMI by Onofri and Ciriofolo
presented a AMMI function. One of arguments for this function AMMI (Page
17) is biplot. There is a biplot fucntion from {stats} package. I guess
they are not the same. Could the authors clarify that?
Thanks,
From the help page:
control: a list of control values ...
Now, control=c(minFactor=1/2048) is not a list but a numeric vector.
So it is warning you about incorrect usage.
On Thu, 26 Apr 2007, Pedro Mardones wrote:
Dear R users;
I was trying to fit a nonlinear model using gnls (nlme version
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