Displaying 4 results from an estimated 4 matches for "nreal".
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2012 Oct 08
0
Mininum number of resamples required to do BCa bootstrap?
...ubscripted syntax is a relic of edited down a
more complex script):
> N <- 25
> s <- rlnorm(N, 0, 1)
> require("boot")
Loading required package: boot
> v <- NULL # hold sample variance estimates
> i <- 1
> v[i] <- var(s) # get sample variance
> nReal <- 10
> varf <- function (x,i) { var(x[i]) }
> fabc <- function (x, w) { # weighted average (biased) variance
+ sum(x^2 * w) / sum(w) - (sum(x * w) / sum(w))^2
+ }
> p <- c(.25, .75, .2, .8, .15, .85, .1, .9, .05, .95, .025, .975,
.005, .995)
> cl <- c(0.5, 0.6, 0....
2012 Oct 02
0
Possible error in BCa method for confidence intervals in package 'boot'
...ubscripted syntax is a relic of edited down a
more complex script):
> N <- 25
> s <- rlnorm(N, 0, 1)
> require("boot")
Loading required package: boot
> v <- NULL # hold sample variance estimates
> i <- 1
> v[i] <- var(s) # get sample variance
> nReal <- 10
> varf <- function (x,i) { var(x[i]) }
> fabc <- function (x, w) { # weighted average (biased) variance
+ sum(x^2 * w) / sum(w) - (sum(x * w) / sum(w))^2
+ }
> p <- c(.25, .75, .2, .8, .15, .85, .1, .9, .05, .95, .025, .975,
.005, .995)
> cl <- c(0.5, 0.6, 0....
2008 Sep 26
0
Confidence interval for binomial variance
...vCI<- c(vest/phiU, vest/phiL) #chi-square-based
}
return (vCI)
}
Here is a test program to measure coverage:
#09.26.08 03.10 tbinomVarCI.r
#copyright 2008 by Robert A LaBudde, all rights reserved
#test of CI for binomial sample variance
#created: 09.26.08 by r.a. labudde
#changes:
nReal <- 1000
for (POD in c(0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.50)) {
cat('\nPOD: ', sprintf('%8.4f', POD), '\n')
for (nRepl in c(6, 12, 20, 50)) {
vtrue<- POD*(1-POD)/nRepl
pcover<- 0
for (iReal in 1:nReal) {
x<- rbinom(1, nRepl,...
2007 Mar 13
0
segfault with correlation structures in nlme
...,
sfix)), pdFactor = as.double(pdFactor(nlmeSt$reStruct)),
as.integer(unlist(rev(grpShrunk))), as.integer(unlist(Dims)),
as.integer(attr(nlmeSt$reStruct, "settings"))[-(1:3)], as.double(cF),
as.double(vW), as.integer(unlist(cD)), settings =
as.double(pnlsSettings), additional = double(NReal * (pLen + 1)),
as.integer(!is.null(correlation)), as.integer(!is.null(weights)),
nlModel, NAOK = TRUE)
6: nlme.formula(model = follicles ~ A + B * sin(2 * pi * w * Time) +
C * cos(2 * pi * w * Time), data = Ovary, fixed = A + B + C + w ~ 1,
random = pdDiag(A + B + w ~ 1), start = c(fixef(f...