invchisq | R Documentation |
Density, distribution function, quantile function and random generation for the inverse chi-squared distribution.
dinvchisq(x, df, ncp = 0, log = FALSE) pinvchisq(q, df, ncp = 0, lower.tail = TRUE, log.p = FALSE) qinvchisq(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE) rinvchisq(n, df, ncp = 0)
x, q |
vector of quantiles. |
df |
degrees of freedom (non-negative, but can be non-integer). |
ncp |
non-centrality parameter (non-negative). |
log, log.p |
logical; if |
lower.tail |
logical; if |
p |
vector of probabilities. |
n |
number of observations. If length(n) > 1, the length is taken to be the number required. |
The functions (d/p/q/r)invchisq()
simply wrap those of the standard
(d/p/q/r)chisq()
R implementation, so look at, say, stats::dchisq()
for
details.
stats::dchisq()
; these functions just wrap the (d/p/q/r)chisq()
functions.
s <- seq(0, 3, .01) plot(s, dinvchisq(s, 3), type = 'l') f <- function(x) dinvchisq(x, 3) q <- 2 integrate(f, 0, q) (p <- pinvchisq(q, 3)) qinvchisq(p, 3) # = q mean(rinvchisq(1e5, 3) <= q) f <- function(x) dinvchisq(x, 3, ncp = 2) q <- 1.5 integrate(f, 0, q) (p <- pinvchisq(q, 3, ncp = 2)) qinvchisq(p, 3, ncp = 2) # = q mean(rinvchisq(1e7, 3, ncp = 2) <= q)
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