https://github.com/cran/aster
Tip revision: aa47935123bfca8a22cbc8345d658d0c1713a289 authored by Charles J. Geyer on 14 December 2023, 15:20:02 UTC
version 1.1-3
version 1.1-3
Tip revision: aa47935
ktnb.R
library(aster)
do.chisq.test <- function(x, alpha, k, mu, max.bin) {
stopifnot(all(x > k))
stopifnot(k + 1 < max.bin)
xx <- seq(k + 1, max.bin)
yy <- dnbinom(xx, size = alpha, mu = mu)
yy[length(yy)] <- pnbinom(max.bin - 1, size = alpha, mu = mu,
lower.tail = FALSE)
pp <- yy / sum(yy)
ecc <- length(x) * pp
if (any(ecc < 5.0))
warning("violates rule of thumb about > 5 expected in each cell")
cc <- tabulate(x, max.bin)
cc <- cc[xx]
cc[length(cc)] <- nsim - sum(cc[- length(cc)])
chisqstat <- sum((cc - ecc)^2 / ecc)
pval <- pchisq(chisqstat, length(ecc) - 1, lower.tail = FALSE)
if (exists("save.min.pval")) {
save.min.pval <<- min(pval, save.min.pval)
save.ntests <<- save.ntests + 1
} else {
save.min.pval <<- pval
save.ntests <<- 1
}
list(chisqstat = chisqstat, df = length(ecc) - 1, pval = pval,
observed = cc, expected = ecc, x = xx)
}
set.seed(42)
nsim <- 1e4
alpha <- 2.222
mu <- 10
k <- 2
x <- rktnb(nsim, alpha, k, mu)
chisqout1 <- do.chisq.test(x, alpha, k, mu, 40)
alpha <- 2.222
mu <- 3.5
k <- 2
x <- rktnb(nsim, alpha, k, mu)
chisqout2 <- do.chisq.test(x, alpha, k, mu, 20)
alpha <- 2.222
mu <- 2.5
k <- 2
x <- rktnb(nsim, alpha, k, mu)
chisqout3 <- do.chisq.test(x, alpha, k, mu, 16)
alpha <- 2.222
mu <- 1.5
k <- 2
x <- rktnb(nsim, alpha, k, mu)
chisqout4 <- do.chisq.test(x, alpha, k, mu, 12)
alpha <- 2.222
mu <- 0.5
k <- 2
x <- rktnb(nsim, alpha, k, mu)
chisqout5 <- do.chisq.test(x, alpha, k, mu, 8)
alpha <- 2.222
mu <- 0.1
k <- 2
x <- rktnb(nsim, alpha, k, mu)
chisqout6 <- do.chisq.test(x, alpha, k, mu, 5)
nsim <- 2e5
alpha <- 2.222
mu <- 0.01
k <- 2
x <- rktnb(nsim, alpha, k, mu)
chisqout7 <- do.chisq.test(x, alpha, k, mu, 5)
alpha <- 2.222
mu <- 1.5
xpred <- 0:10
save.seed <- .Random.seed
x <- rktp(xpred, k, mu, xpred)
.Random.seed <- save.seed
my.x <- rep(0, length(xpred))
for (i in seq(along = xpred))
if (xpred[i] > 0)
for (j in 1:xpred[i])
my.x[i] <- my.x[i] + rktp(1, k, mu)
all.equal(x, my.x)
nsim <- 1e4
alpha <- 5.55
k <- 5
mu <- pi
x <- rktnb(nsim, alpha, k, mu)
chisqout8 <- do.chisq.test(x, alpha, k, mu, 16)
alpha <- 5.55
k <- 10
mu <- exp(2)
x <- rktnb(nsim, alpha, k, mu)
chisqout9 <- do.chisq.test(x, alpha, k, mu, 29)
cat("number of tests:", save.ntests, "\n")
save.ntests * save.min.pval > 0.05
#####
set.seed(42)
nind <- 25
nnode <- 1
ncoef <- 1
alpha <- 3.333
k <- 2
pred <- 0
fam <- 1
ifam <- fam.truncated.negative.binomial(size = alpha, trunc = k)
aster:::setfam(list(ifam))
theta.origin <- aster:::getfam()[[1]]$origin
theta <- (- 4 / 3)
p <- 1 - exp(theta)
x <- rnbinom(1000, size = alpha, prob = p)
x <- x[x > k]
x <- x[1:nind]
modmat <- matrix(1, nrow = nind, ncol = 1)
out <- mlogl(theta - theta.origin, pred, fam, x, modmat, modmat,
deriv = 2, type = "conditional", famlist = list(ifam))
xxx <- seq(0, 100)
ppp <- dnbinom(xxx, size = alpha, prob = p)
ppp[xxx <= k] <- 0
ppp <- ppp / sum(ppp)
tau <- sum(xxx * ppp)
my.grad.logl <- sum(x - tau)
all.equal(- out$gradient, my.grad.logl)
my.fish.info <- length(x) * sum((xxx - tau)^2 * ppp)
all.equal(as.numeric(out$hessian), my.fish.info)
foo <- new.env(parent = emptyenv())
bar <- suppressWarnings(try(load("ktnb.rda", foo), silent = TRUE))
if (inherits(bar, "try-error")) {
save(list = c(paste("chisqout", 1:9, sep = ""), "out"), file = "ktnb.rda")
} else {
print(all.equal(chisqout1, foo$chisqout1))
print(all.equal(chisqout2, foo$chisqout2))
print(all.equal(chisqout3, foo$chisqout3))
print(all.equal(chisqout4, foo$chisqout4))
print(all.equal(chisqout5, foo$chisqout5))
print(all.equal(chisqout6, foo$chisqout6))
print(all.equal(chisqout7, foo$chisqout7))
print(all.equal(chisqout8, foo$chisqout8))
print(all.equal(chisqout9, foo$chisqout9))
print(all.equal(out, foo$out))
}