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Density, distribution function, and random generation for the Birnbaum-Saunders distribution.
dbisa(x, scale = 1, shape, log = FALSE)
pbisa(q, scale = 1, shape, lower.tail = TRUE, log.p = FALSE)
qbisa(p, scale = 1, shape, lower.tail = TRUE, log.p = FALSE)
rbisa(n, scale = 1, shape)
dbisa
gives the density,
pbisa
gives the distribution function, and
qbisa
gives the quantile function, and
rbisa
generates random deviates.
T. W. Yee and Kai Huang
The Birnbaum-Saunders distribution
is a distribution which is used in survival analysis.
See bisa
, the VGAM family function
for estimating the parameters,
for more details.
bisa
.
if (FALSE) {
x <- seq(0, 6, len = 400)
plot(x, dbisa(x, shape = 1), type = "l", col = "blue",
ylab = "Density", lwd = 2, ylim = c(0,1.3), lty = 3,
main = "X ~ Birnbaum-Saunders(shape, scale = 1)")
lines(x, dbisa(x, shape = 2), col = "orange", lty = 2, lwd = 2)
lines(x, dbisa(x, shape = 0.5), col = "green", lty = 1, lwd = 2)
legend(x = 3, y = 0.9, legend = paste("shape = ",c(0.5, 1,2)),
col = c("green","blue","orange"), lty = 1:3, lwd = 2)
shape <- 1; x <- seq(0.0, 4, len = 401)
plot(x, dbisa(x, shape = shape), type = "l", col = "blue",
main = "Blue is density, orange is the CDF", las = 1,
sub = "Red lines are the 10,20,...,90 percentiles",
ylab = "", ylim = 0:1)
abline(h = 0, col = "blue", lty = 2)
lines(x, pbisa(x, shape = shape), col = "orange")
probs <- seq(0.1, 0.9, by = 0.1)
Q <- qbisa(probs, shape = shape)
lines(Q, dbisa(Q, shape = shape), col = "red", lty = 3, type = "h")
pbisa(Q, shape = shape) - probs # Should be all zero
abline(h = probs, col = "red", lty = 3)
lines(Q, pbisa(Q, shape = shape), col = "red", lty = 3, type = "h")
}
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