The Birnbaum-Saunders Distribution
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)
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.
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
bisa.
## Not run:
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", las = 1, ylab = "",
main = "Blue is density, orange is cumulative distribution function",
sub = "Purple lines are the 10,20,...,90 percentiles", 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 = "purple", lty = 3, type = "h")
pbisa(Q, shape = shape) - probs # Should be all zero
abline(h = probs, col = "purple", lty = 3)
lines(Q, pbisa(Q, shape = shape), col = "purple", lty = 3, type = "h")
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.