| FDist {stats} | R Documentation |
Density, distribution function, quantile function and random
generation for the F distribution with df1 and df2
degrees of freedom (and optional non-centrality parameter ncp).
df(x, df1, df2, ncp, log = FALSE)
pf(q, df1, df2, ncp, lower.tail = TRUE, log.p = FALSE)
qf(p, df1, df2, ncp, lower.tail = TRUE, log.p = FALSE)
rf(n, df1, df2, ncp)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
df1, df2 |
degrees of freedom. |
ncp |
non-centrality parameter. If omitted the central F is assumed. |
log, log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are
|
The F distribution with df1 = n_1 and df2 =
n_2 degrees of freedom has density
f(x) = \frac{\Gamma(n_1/2 + n_2/2)}{\Gamma(n_1/2)\Gamma(n_2/2)}
\left(\frac{n_1}{n_2}\right)^{n_1/2} x^{n_1/2 -1}
\left(1 + \frac{n_1 x}{n_2}\right)^{-(n_1 + n_2) / 2}%
for x > 0.
It is the distribution of the ratio of the mean squares of
n_1 and n_2 independent standard normals, and hence
of the ratio of two independent chi-squared variates each divided by its
degrees of freedom. Since the ratio of a normal and the root
mean-square of m independent normals has a Student's t_m
distribution, the square of a t_m variate has a F distribution on
1 and m degrees of freedom.
The non-central F distribution is again the ratio of mean squares of
independent normals of unit variance, but those in the numerator are
allowed to have non-zero means and ncp is the sum of squares of
the means. See Chisquare for further details on
non-central distributions.
df gives the density,
pf gives the distribution function
qf gives the quantile function, and
rf generates random deviates.
Invalid arguments will result in return value NaN, with a warning.
For df, and ncp == 0, computed via a binomial
probability, code contributed by Catherine Loader (see dbinom);
for ncp != 0, computed via a dbeta,
code contributed by Peter Ruckdeschel.
For pf, via pbeta (or for large df2, via
pchisq).
For qf, via qchisq for large df2, else via
qbeta.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, volume 2, chapters 27 and 30. Wiley, New York.
dchisq for chi-squared and dt for Student's
t distributions.
## the density of the square of a t_m is 2*dt(x, m)/(2*x)
# check this is the same as the density of F_{1,m}
x <- seq(0.001, 5, len=100)
all.equal(df(x^2, 1, 5), dt(x, 5)/x)
## Identity: qf(2*p - 1, 1, df)) == qt(p, df)^2) for p >= 1/2
p <- seq(1/2, .99, length=50); df <- 10
rel.err <- function(x,y) ifelse(x==y,0, abs(x-y)/mean(abs(c(x,y))))
quantile(rel.err(qf(2*p - 1, df1=1, df2=df), qt(p, df)^2), .90)# ~= 7e-9