On Fri, 2 Sep 2005, Sean Davis wrote:
> I have a simply defined regions ([start,end] where start large sets of them and want to find all regions in the first that overlap
> any regions in the second. The closest I could find by searching is
> overlap.owin in I can do this by looping, but there is
Does the following help:
> set.seed(1)
> pop <- 1:100
> s1 <- sample(pop, 5)
> s1. <- pop[!(pop %in% s1)]
> s2 <- sample(s1., 5)
> s1
[1] 27 37 57 89 20
> s2
[1] 91 94 66 62 6
>
spencer graves
[EMAIL PROTECTED] wrote:
>
> Hello,
> I have a problem to treat my dat
Does the following help you:
library(MASS)
set.seed(1)
x1 <- rep(1:2, 2)
x2 <- rep(1:2, each=2)
DF <- data.frame(x1=x1, x2=x2, y=rbinom(4, 1000,
prob=(x1+x2-1.5)/3)/1000)
fit0 <- glm(y~1, family=binomial, data=DF,
weights=rep(1000, 4))
stepAIC(fit0, y~x1+x2)
Hi all, I've been trying to get a large (12mb) Stata
survey database into R. I managed that, but when I
attach survey weights, something goes wrong. The error
message is: object dchina not found. Here's the
script:
library(car)
library(foreign)
library(survey)
China <- read.dta("C:/final07c2.dta"
Since I have not seen a reply to this question, I will offer
something: The problem with testing interactions before main effects is
that it's not clear what interactions even mean without the main
effects. This is true in virtually any context, not just lme.
Consider the
> On 02/09/05, Nam-Ky Nguyen <[EMAIL PROTECTED]> wrote:
>> > b) You do NOT want to do numerical computations on software available
>> in
>> > Java byte code.
>> You do not want to do heavy numerical computations with R either. Most
>> statistical calculation using R requires a fraction of a second
On 3 September 2005 at 17:59, Justin Rhodes wrote:
| Dear R-help,
|
| Can someone please help me discover what function or code will give
| me a p-value from the input: 1) R-square statistic from a simple
| linear regression, and 2) sample size, n
|
| This would be greatly appreciated. I need
Dear R-help,
Can someone please help me discover what function or code will give
me a p-value from the input: 1) R-square statistic from a simple
linear regression, and 2) sample size, n
This would be greatly appreciated. I need this because I am using a
database that gives me R-square and sa
> "Duncan" == Duncan Murdoch <[EMAIL PROTECTED]>
> on Sat, 03 Sep 2005 11:40:18 -0400 writes:
Duncan> John Sorkin wrote:
>> A question for R (and perhaps S and SPlus) historians.
>>
>> Does anyone know the reason for the inconsistency in the
>> way that the action
Adaikalavan Ramasamy wrote:
> Thank you ! So to be absolutely sure, the C-index in my case is
> 0.5 * ( 0.3634 + 1 ) = 0.6817 right ?
correct
>
> If the above calculation is correct then why do I get the following :
>
> rcorr.cens( predict(fit), Surv( GBSG$rfst, GBSG$cens ) )[ "C Index" ]
I am trying to use the RSvgDevice package to produce some SVG graphs which I
want to edit with Inkscape 0.42.
Under Linux (Kubuntu 5.04) I use the following:
library(RSvgDevice)
plot(1:10, 1:10)
devSVG(file = "/home/adi/Rplots.svg", width = 10, height = 8,
bg = "white", fg = "black", onefile=TRU
Adrian DUSA gmail.com> writes:
> I am trying to use the RSvgDevice package to produce some SVG graphs which I
> want to edit with Inkscape 0.42.
> [...snip...]
Argh, a minute after posting a find out the solution here:
http://www.stat.auckland.ac.nz/~paul/Talks/gridSVG/slide8.html
It works bri
John Sorkin wrote:
> A question for R (and perhaps S and SPlus) historians.
>
> Does anyone know the reason for the inconsistency in the way that the
> action that should be taken when data are missing is specified? There
> are several variants, na.action, na.omit, "T", TRUE, etc. I know that a
>
A question for R (and perhaps S and SPlus) historians.
Does anyone know the reason for the inconsistency in the way that the
action that should be taken when data are missing is specified? There
are several variants, na.action, na.omit, "T", TRUE, etc. I know that a
foolish consistency is the hob
Thank you ! So to be absolutely sure, the C-index in my case is
0.5 * ( 0.3634 + 1 ) = 0.6817 right ?
If the above calculation is correct then why do I get the following :
rcorr.cens( predict(fit), Surv( GBSG$rfst, GBSG$cens ) )[ "C Index" ]
C Index
0.3115156
( I am aware that is a re
Hi ti all,
I am using psm() function of the Design package with dist=' weibull '.
I think that Design uses the following weibull R*L*(R*L)^(R-1). Using psm()
with dist= "weibull" I get estimates of Log(scale) and intercept . Does anybody
knows the relation between R, L and intercept and scale pa
He you checked this example?:
z <- ts(matrix(rt(200 * 8, df = 3), 200, 8), start = c(1961,1), frequency =
12)
z <- window(z[, 1:3], end = c(1969, 12))
plot(z, plot.type = "single", lty = 1:3, col = 4:2)
It is from the help page of plot.ts
Felipe
"Jose Augusto Jr - jamaj - terra" <[EMAIL PROTEC
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