Density, CDF, quantile function and random number generator for the Inverse Chi--squared distribution.
dinv.chisq(x, df, log = FALSE)
pinv.chisq(q, df, lower.tail = TRUE, log.p = FALSE)
qinv.chisq(p, df, lower.tail = TRUE, log.p = FALSE)
rinv.chisq(n, df)
dinv.chisq
returns the density, pinv.chisq
returns the
distribution function, qinv.chisq
gives the quantiles, and
rinv.chisq
generates random numbers from this distribution.
V. Miranda
The inverse chi--squared distribution with non--negative
df
= gamma
function.
The mean is
Also, as with Chisquare
, the degrees
of freedom can be non--integer.
Johnson, N.L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions. Chapters 18 (volume 1) and 29 (volume 2). Wiley, New York.
## Example 1 ##
nn <- 50; df <- 1.4
data.1 <- ppoints(nn)
data.q <- qinv.chisq(-data.1, df = df, log.p = TRUE)
data.p <- -log(pinv.chisq(data.q, df = df))
max(abs(data.p - data.1)) # Should be zero
# \donttest{
## Example 2 ##
xx <- seq(0, 3.0, len = 301)
yy <- dinv.chisq(xx, df = df)
qtl <- seq(0.1, 0.9, by = 0.1)
d.qtl <- qinv.chisq(qtl, df = df)
plot(xx, yy, type = "l", col = "orange",
main = "Orange is density, blue is cumulative distribution function",
sub = "Brown dashed lines represent the 10th, ... 90th percentiles",
las = 1, xlab = "x", ylab = "", ylim = c(0, 1))
abline(h = 0, col= "navy", lty = 2)
lines(xx, pinv.chisq(xx, df = df), col = "blue")
lines(d.qtl, dinv.chisq(d.qtl, df = df), type ="h", col = "brown", lty = 3)
# }
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