Wolski [EMAIL PROTECTED] wrotes:
I have seen the output and it does not matter to me anymore if prompt
or package.skeleton works on any platform. I hope it wasn't a too big
heresy. If someone would ask me what are the week point of R, then
the only one that pops up immediately, is that the
Philippe Grosjean [EMAIL PROTECTED] writes:
Well, writing a quick and durty help for a function with a few lines of
comment above or below the function code (a la Matlab) should be nice. I
don't think that it should be a good idea to provide a complex alternative
solution for documenting the
Hi
How can one simulate correlated distributions in R for windows?
Coomaren P. Vencatasawmy
-
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DVD
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Assuming you are measuring Y and you have factor A fixed and factor B
random, I would create a model like:
mod-lme(Y ~ A, random=~1|B/A, mydata)
VarCorr(mod1)
the term random=~1|B tells the model that B is a random factor, adding
the /A to get random =~1|B/A tells the model you want the
Hi,
I'm trying to understand the complete linkage method in hclust. Can anyone provide a
breakdown of the formula (p9 of the pdf documentation) or tell me what the sup
operator does/means?
thanks in advance
Tom
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Thanks!
I think the minor differences taking the values with
rnorm result of the homogen distribution without an
effect. But the results of aov and lme should be
similiar for data with effects, too (at least for
simple and balanced designs).
Karl
--- Pascal A. Niklaus [EMAIL PROTECTED]
schrieb:
On Wed, 3 Dec 2003 10:08:04 + (GMT), you wrote:
Hi
How can one simulate correlated distributions in R for windows?
I'm not sure exactly what you're asking, but maybe the MASS function
mvrnorm() is what you want.
Duncan Murdoch
__
[EMAIL
Hello,
I've got a big problem. I'm using R for geostatistical analyses, especially
the field-package.
I try to generate plots after the kriging process with help of
image.plot(..., col=terrain.colors, ...). Everything works fine, but I want
to reverse the color-palettes (heat.colors, topo.colors
On Wed, 3 Dec 2003, Lars Peters wrote:
Hello,
I've got a big problem. I'm using R for geostatistical analyses, especially
the field-package.
I try to generate plots after the kriging process with help of
image.plot(..., col=terrain.colors, ...). Everything works fine, but I want
to
Your recommendations have worked great. I have found both cut and
ifelse to be useful.
I have one more question. When should I use factors over a character
vector. I know that they have different uses. However, I am still
trying to figure out how I can best take advantage of factors.
The
Hello,
Is there a test for independence available based on a multidimensional
contingency table?
I've about 300 processes, and for each of them I get numbers for failures and
successes. I've two or more conditions under which I test these processes.
If I had just one process to test I could
Hi,
Can R calculate an intraclass correlation coefficient for clustered data,
when the outcome variable is dichotomous?
By now I calculate it by hand, estimating between- and intracluster variance
by one-way ANOVA - however I don't feel very comfortable about this, since
the distributional
On Wed, 3 Dec 2003, Arend P. van der Veen wrote:
Your recommendations have worked great. I have found both cut and
ifelse to be useful.
I have one more question. When should I use factors over a character
vector. I know that they have different uses. However, I am still
trying to figure
Hi,
I'm clustering objects defined by categorical variables with a hierarchical
algorithm - average linkage.
My distance matrix (general dissimilarity coefficient) includes several
distances with exactly the same values.
As I see, a standard agglomerative procedure ignores this problems, simply
On Wed, 2003-12-03 at 14:34, [EMAIL PROTECTED] wrote:
Is there a test for independence available based on a multidimensional
contingency table?
I've about 300 processes, and for each of them I get numbers for failures and
successes. I've two or more conditions under which I test these
Bruno -
Many people add a tiny random number to each of the distances,
or deliberately randomize the input order. This means that
any clustering is not reproducible, unless you go back to the
original randoms, but it forces you not to pay attention to
minor differences.
Ah, I think you're
On Wed, 3 Dec 2003, Bruno Giordano wrote:
Hi,
I'm clustering objects defined by categorical variables with a hierarchical
algorithm - average linkage.
My distance matrix (general dissimilarity coefficient) includes several
distances with exactly the same values.
As I see, a standard
Hi,
Brian Ripley already replied don't use average linkage... You
may think about k-medoid (pam) in package cluster instead.
However, often average linkage is not such a bad choice, and if you really
want to use it for your data, you may try the following:
Among the hierarchical methods, single
What I did was, in presence of equal values distances, to randomize the
selection of them, and compute the distortion of the solution using
cophenetic correlation.
I computed 1 random trees for each of three methods: average, single
and complete linkage.
Among the randomly selected solutions,
Thomas Stabla wrote:
Hello,
I have defined a new class
setClass(myclass, representation(min = numeric, max = numeric))
and want to write accessor functions, so that for
foo = new(myclass, min = 0, max = 1)
min(foo) # prints 0
max(foo) # prints 1
At first i created a generic
I have been using a little function I wrote myself; look at
http://www.unc.edu/home/aperrin/tips/src/icc.R for the code. Not pretty,
but it works.
ap
--
Andrew J Perrin - http://www.unc.edu/~aperrin
Assistant Professor of
Christian --
You don't provide enough information (like a call) to answer this. I
suspect, though, that you may be subsetting in a way that passes
randomForest no data.
I'm not aware offhand of an easy way to get this error from randomForest. I
tried creating some data superficially similar
Thank you Frank and Gabor for the fixes and checking and rechecking!
Everything seems to work well with the Hmisc functions tried--upData, describe
and summary.
To summarize:
1. Add the testDateTime and formatDateTime functions (copied from Frank's
messages) to the Hmisc file (or run prior to
Hmmm, thanks for your suggestions i'm in the
same opinion with any subsetting problem, but curious is
that my model i.e. with library(gbm) or simple lm works,
because my task is to find out the weights/importance values
for the attributes and i would like compare the results between
the
Hi,
I have a rectangular matrix and I need to check whether any columns
are identical or not. Currently I'm looping over the columns and
checking each column with all the others with identical().
However, as experience has shown me, getting rid of loops is a good idea
:) Would anybody have any
On Wed, 2003-12-03 at 12:06, Rajarshi Guha wrote:
Hi,
I have a rectangular matrix and I need to check whether any columns
are identical or not. Currently I'm looping over the columns and
checking each column with all the others with identical().
However, as experience has shown me,
On Wed, 3 Dec 2003, Rajarshi Guha wrote:
Hi,
I have an apply statement that looks like:
check.cols - function(v1, v2) {
+ return( identical(v1,v2) );
+ }
x
[,1] [,2] [,3]
[1,]133
[2,]454
[3,]276
apply(x, c(2), check.cols, v2=c(7,8,9))
It looks like you are trying to fit Schaeffer model (a special case of the
Pella-Tomlinsion general production model) to the data. Such models can be
solved in a completely general way using ADModel Builder, and an example of
the general production model application can be found at
From: Rajarshi Guha
On Wed, 2003-12-03 at 13:18, J.R. Lockwood wrote:
list will come up with something clever. the other issues
is that you
need to be careful when doing equality comparisons with
floating point
numbers. unless your matrix consists of characters or integers,
Rajarshi Guha [EMAIL PROTECTED] writes:
apply(x, c(2), funtion(v1,v2){ identical(v1,v2) }, v2=c(1,4,2))
The above gives me a syntax error. I also tried:
No wonder! Try with `function' instead of `funtion'.
--
Bjørn-Helge Mevik
__
[EMAIL
I have a 3d irregular grid of a surface (closed surface)
I would like to calculate the volume enclosed inside this surface
can this be done in R
any help is very much appreciated
best regards
karim
Karim
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[EMAIL PROTECTED] mailing list
What is the context? What do the outliers represent? If you
think carefully about the context, you may find the answer.
hope this helps. spencer graves
p.s. I know statisticians who worked for HP before the split and who
still work for either HP or Agilent, I'm not certain which.
Richard Bonneau [EMAIL PROTECTED] writes:
Hi,
I'm using gl1ce with family=binomial like so:
yy
succ fail
[1,] 76 23
[2,] 32 67
[3,] 56 43
...
[24,] 81 18
xx
c1219 c643
X1 0.04545455 0.64274145
X2 0.17723669 0.90392792
...
X24
If you know that the line should pass through (0,0), would it make sense to
do a regression without an intercept? You can do that by putting -1 in
the formula, like: lm(y ~ x - 1).
Hope this helps,
Matt
Matthew Wiener
RY84-202
Applied Computer Science Mathematics Dept.
Merck Research Labs
Not a good idea, unless the regression function is *known* to be linear.
More likely it is only approximately linear over small ranges.
Murray Jorgensen
Wiener, Matthew wrote:
If you know that the line should pass through (0,0), would it make sense to
do a regression without an intercept? You
It is likely that the true relationship is nonlinear. There isn't a priori knowledge
about linearity. In the small range where we do have enough data, the relationship
looks linear. Outside the range, the data are very scarse and have high level of
noises too.
This is why adding (0,0) to the
Hi--
While I agree that we cannot agree on the ideal algorithms, we should be
taking practical steps to implement microarrays in the clinic. I think
we can all agree that our algorithms have some degree of efficacy over
and above conventional diagnostic techniques. If patients are dying
from
Michael Benjamin [EMAIL PROTECTED] writes:
I was just looking ahead two or three years--where is all this genomic
array research headed? I guess I'm concerned about scalability.
Me too -- but at least in the near future, data will be growing more
than the capacity to process it.
Is anyone
[EMAIL PROTECTED] wrote:
Hi,
MY question is like the following:
I would like to have a robust regression line. The data I have are
mostly clustered around a small range. So
the regression line tend to be influenced strongly by outlier points
(with large cook's distance). From the
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