TDist {stats} R Documentation

## The Student t Distribution

### Description

Density, distribution function, quantile function and random generation for the t distribution with `df` degrees of freedom (and optional non-centrality parameter `ncp`).

### Usage

```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)
```

### Arguments

 `x, q` vector of quantiles. `p` vector of probabilities. `n` number of observations. If `length(n) > 1`, the length is taken to be the number required. `df` degrees of freedom (> 0, maybe non-integer). ```df = Inf``` is allowed. `ncp` non-centrality parameter delta; currently except for `rt()`, only for `abs(ncp) <= 37.62`. If omitted, use the central t distribution. `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].

### Details

The t distribution with `df` = n degrees of freedom has density

f(x) = Gamma((n+1)/2) / (sqrt(n pi) Gamma(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) / sqrt(V/df) where U and V are independent random variables, U ~ N(0,1) and V ~ Chi^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(mu, sigma^2) Then T is distributed as non-central t with `df`{} = n-1 degrees of freedom and non-centrality parameter `ncp`= (mu - m0) * sqrt(n)/sigma.

### Value

`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.

### Note

Setting `ncp = 0` is not equivalent to omitting `ncp`. R uses the non-centrality functionality whenever `ncp` is specified which provides continuous behavior at ncp = 0.

### Source

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`, 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.

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.

### References

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.

`df` for the F distribution.

### Examples

```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")
```

[Package stats version 2.9.0 Index]