| TDist {stats} | R Documentation |
Density, distribution function, quantile function and random
generation for the t distribution with df degrees of freedom
(and optional non-centrality parameter ncp).
dt(x, df, ncp, log = FALSE) pt(q, df, ncp, lower.tail = TRUE, log.p = FALSE) qt(p, df, ncp, lower.tail = TRUE, log.p = FALSE) rt(n, df, ncp)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
df |
degrees of freedom (> 0, maybe non-integer). |
ncp |
non-centrality parameter delta;
currently except for |
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 t distribution with df = n degrees of
freedom has density
f(x) = Γ((n+1)/2) / (√(n π) Γ(n/2)) (1 + x^2/n)^-((n+1)/2)
for all real x. It has mean 0 (for n > 1) and variance n/(n-2) (for n > 2).
The general non-central t
with parameters (df, Del) = (df, ncp)
is defined as the distribution of
T(df, Del) := (U + Del) / √(V/df)
where U and V are independent random
variables, U ~ N(0,1) and
V ~ χ^2(df) (see Chisquare).
The most used applications are power calculations for t-tests:
Let T= (mX - m0) / (S/sqrt(n))
where
mX is the mean and S the sample standard
deviation (sd) of X_1, X_2, …, X_n which are
i.i.d. N(μ, σ^2)
Then T is distributed as non-central t with
df= n - 1
degrees of freedom and non-centrality parameter
ncp = (μ - m0) * sqrt(n)/σ.
dt gives the density,
pt gives the distribution function,
qt gives the quantile function, and
rt 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.
The central dt is computed via an accurate formula
provided by Catherine Loader (see the reference in dbinom).
For the non-central case of dt, C code contributed by
Claus Ekstrøm based on the relationship (for
x != 0) to the cumulative distribution.
For the central case of pt, a normal approximation in the
tails, otherwise via pbeta.
For the non-central case of pt based on a C translation of
Lenth, R. V. (1989). Algorithm AS 243 — Cumulative distribution function of the non-central t distribution, Applied Statistics 38, 185–189.
This computes the lower tail only, so the upper tail suffers from cancellation and a warning will be given when this is likely to be significant.
For central qt, a C translation of
Hill, G. W. (1970) Algorithm 396: Student's t-quantiles. Communications of the ACM, 13(10), 619–620.
altered to take account of
Hill, G. W. (1981) Remark on Algorithm 396, ACM Transactions on Mathematical Software, 7, 250–1.
The non-central case is done by inversion.
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole. (Except non-central versions.)
Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, volume 2, chapters 28 and 31. Wiley, New York.
Distributions for other standard distributions, including
df for the F distribution.
require(graphics)
1 - pt(1:5, df = 1)
qt(.975, df = c(1:10,20,50,100,1000))
tt <- seq(0,10, len=21)
ncp <- seq(0,6, len=31)
ptn <- outer(tt,ncp, function(t,d) pt(t, df = 3, ncp=d))
t.tit <- "Non-central t - Probabilities"
image(tt,ncp,ptn, zlim=c(0,1), main = t.tit)
persp(tt,ncp,ptn, zlim=0:1, r=2, phi=20, theta=200, main=t.tit,
xlab = "t", ylab = "non-centrality parameter",
zlab = "Pr(T <= t)")
plot(function(x) dt(x, df = 3, ncp = 2), -3, 11, ylim = c(0, 0.32),
main="Non-central t - Density", yaxs="i")