On Thu, Mar 31, 2011 at 5:49 PM, array chip wrote:
> Thanks Bernd! I tried your approach with my real example, sometimes it worked,
> sometimes it didn't. For example
>
> grep('[^(arg)]\\.symptom',"stomach.symptom",value=T)
> [1] "stomach.symptom"
>
> grep('[^(arg)]\\.symptom',"liver.symptom",valu
Ok then this code didn't do what I wanted. I want "not including 'arg' before
'.symptom'", not individual letters of "arg", but rather as a word.
Bill Dunlap suggested using invert=T, it works for single 1 condition, but not
for 2 conditions here: not including "arg" before ".", but at the same
Am 31.03.2011 21:06, schrieb array chip:
Ok then this code didn't do what I wanted. I want "not including
'arg' before '.symptom'", not individual letters of "arg", but rather
as a word.
Bill Dunlap suggested using invert=T, it works for single 1
condition, but not for 2 conditions here: not inc
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
> Behalf Of stephen sefick
> Sent: March-31-11 3:38 PM
> To: R help
> Subject: [R] Linear Model with curve fitting parameter?
>
> I have a model Q=K*A*(R^r)*(S^s)
>
> A, R, and S are data
Dear Sir/Madam
This is vogue clothing co., from China.
We are looking for long-cooperation fashion purchaser like you sincerely.
Our products is as following,
1/Mens
Business suits, blazers, sports coats, uniforms, jumpers and shirts. etc.
2/Ladys
Tops, dress, cardigan, skir
Hello Everyone,
I'm learning how to perform various statistical analyses in R. I'm checking my
understanding by replicating examples from my SAS books. Below is an attempt to
replicate a Cox Proportional Hazards model with a time-varying covariate. I
think I'm doing this correctly but am not c
Dear Tomas,
Write the model as
mreg01 = lm(enep1 ~ enpres * proximity1), data=a90)
That is, it's not necessary to index a90 as a list since it's given as the data
argument to lm, and doing so confuses the effect() function. Also,
enpres*proximity1 will include both the enpres:proximity1 in
okay, I found a solution:
library(lattice)
f <- function(x) 1/((1-x[1])*(1-x[2])+1)
u <- seq(0, 1, length.out=20)
grid <- expand.grid(x=u, y=u)
x <- grid[,1]
y <- grid[,2]
z <- apply(grid, 1, f)
pt.x <- c(0.4, 0.7)
pt.y <- c(0.6, 0.8)
eps <- 0.4
pts <- rbind(c(pt.x, f(pt.x)-eps), c(pt.y, f(pt.y
Thanks Bert.
I have read "?formula" again and again, and I'm still struggling;
>lm(body_length ~ head_length-1)
This removes intercept from each individual regression (for male, female,
unknown).
When they are taken together,
>lm(body_length ~ sex*head_length)
This shows differences in slope
HI all,
I am trying to compute the EOF of a matrix using prcomp but unable to get
the expansion co-efficients.
is it possible using prcomp or are there any other methods
thanks
nuncio
--
Nuncio.M
Research Scientist
National Center for Antarctic and Ocean research
Head land Sada
Vasco da Gamma
Go
> -Mensaje original-
> De: Peter Ehlers [mailto:ehl...@ucalgary.ca]
> Enviado el: jueves, 31 de marzo de 2011 18:09
> Para: Rubén Roa
> CC: r-help@r-project.org
> Asunto: Re: [R] Simple lattice question
>
> On 2011-03-31 06:58, Rubén Roa wrote:
> > Thanks Peters!
> >
> > Just a few minor
101 - 111 of 111 matches
Mail list logo