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)
vector of quantiles.
vector of probabilities.
Same as in runif
.
the (positive) scale and shape parameters.
Logical.
If TRUE
then the logarithm of the density is returned.
dbisa
gives the density,
pbisa
gives the distribution function, and
qbisa
gives the quantile function, and
rbisa
generates random deviates.
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
.
# 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")
# }
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