On Wed, 4 Apr 2007, Giuseppe Brundu wrote:
I wonder if there is any tutorial explaining, step by step, how to convert a
(georeferenced) map boundary (from esri shape-file) into a Spatstat window,
for performing the analysis of marked point patterns surveyed inside that
map. Any help on the
And what does this have to do with the lead thread subject line Transition
Matrices?
I guess the only way we can start enforcing thread discipline is to stop
responding to threads that hijack others?
Any thoughts?
Ranjan
On Wed, 4 Apr 2007 11:31:33 +0200 Giuseppe Brundu [EMAIL PROTECTED]
Have you considered the sem package (for structural equation
modeling), which (to me at least) is a generalizion of MANOVA with
canonical analysis. Alternatively, have you considered partial least
squares (e.g., packages pls or plsgenomics)? I haven't used them,
but they sound like
library(R.oo)
ll()
member data.class dimension object.size
1 anumeric 10004028
2author character 1 112
3 expnumeric 1 36
4 last.warning list 2 488
5object function
Another possibility is eapply where I have used naCount
from Henrik's solution:
prop - function(x)
list(class = data.class(x), dim = dim(x), size = object.size(x), NAs
= naCount(x))
do.call(rbind, eapply(.GlobalEnv, prop))
On 3/8/06, Henrik Bengtsson [EMAIL PROTECTED] wrote:
library(R.oo)
It looks like factanal is unable to optimize from these starting values
(kinda like the error message says). So, factanal.fit.mle isn't converging
and you have problems with your analysis. Try putting control = list(trace =
T) in your code to see what happenens. E.g.,
R
R v1 -
Also, factanal() does not do `principal component analysis', and it may
well be that the data are inappropriate for factor analysis if they are
appropriate for PCA.
If PCA was really intended, prcomp() and princomp() are appropriate tools.
On Tue, 30 Nov 2004, Andy Bunn wrote:
It looks like
Please DO READ the section Details in help(nls).
- tom blackwell - u michigan medical school - ann arbor -
__
[EMAIL PROTECTED] mailing list
https://www.stat.math.ethz.ch/mailman/listinfo/r-help
Thanks for providing data and sample command. (In the future, please
provide the data in the form of a data.frame commmand. It is not as
easy to read visually, but it is easier for others to copy into R.)
Please follow Doug Bates' advice:
1. Plot the data.
2. Play with the
1) Please plot your data. Notice that they are of the form y = x.
That is, they fall on a straight line.
2) Please plot the curve that you expect to fit. You could do this,
as I have said in previous replies, by
library(nls)
example(SSfpl)
3) Please plot the four-parameter logistic with
I assume you mean the following:
chemYield -
function(a, x)(a[1]+(a[3]-a[2])/(1+exp(-a[2]*(x-a[4]))
If you want to estimate parameters a[1:4] from data on pairs of (x,
y=chemYield), create a data.frame(x, y), and estimate the parameter
vector a using nls.
If you have trouble getting nls
The most commonly used dose-response functions for nonlinear calibration
curves are the four- and five-parameter logistic functions. The four-
parameter logistic is specified as
F(z) = delta + (alpha - delta)/(1 + (z/gamma)^beta)
so I'm not sure where you are getting your dose-response
Calandra's dose-response function is very close to what you wrote:
She has x = ln(z+1), while x = ln(z) and m = ln(gamma) would give what
you wrote. I would guess that your comments and references should help
her.
Spencer Graves
Paul, David A wrote:
The most commonly used dose-response
Andrea Calandra [EMAIL PROTECTED] writes:
I'm a student in chemical engineering, and i have to implement an algoritm about
FIVE PARAMETERS INTERPOLATION for a calibration curve (dose, optical density)
y = a + (c - a) /(1+ e[-b(x-m])
where
x = ln(analyte dose + 1)
y = the optical
14 matches
Mail list logo
