| 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 P[X ≤ x], otherwise, P[X > x]. |
The F distribution with df1 = n1 and df2 =
n2 degrees of freedom has density
f(x) = Γ((n1 + n2)/2) / (Γ(n1/2) Γ(n2/2)) (n1/n2)^(n1/2) x^(n1/2 - 1) (1 + (n1/n2) x)^-(n1 + n2)/2
for x > 0.
It is the distribution of the ratio of the mean squares of n1 and n2 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.
Supplying ncp = 0 uses the algorithm for the non-central
distribution, which is not the same algorithm used if ncp is
omitted. This is to give consistent behaviour in extreme cases with
values of ncp very near zero.
The code for non-zero ncp is principally intended to be used
for moderate values of ncp: it will not be highly accurate,
especially in the tails, for large values.
For the central case of df, computed via a binomial
probability, code contributed by Catherine Loader (see
dbinom); for the non-central case computed via
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.
Distributions for other standard distributions, including
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