Chisquare {stats} | R Documentation

## The (non-central) Chi-Squared Distribution

### Description

Density, distribution function, quantile function and random generation for the chi-squared (chi^2) distribution with `df` degrees of freedom and optional non-centrality parameter `ncp`.

### Usage

    dchisq(x, df, ncp = 0, log = FALSE)
    pchisq(q, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)
    qchisq(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)
    rchisq(n, df, ncp = 0)

### 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 (non-negative, but can be non-integer).
`ncp` | non-centrality parameter (non-negative).
`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 chi-squared distribution with `df`= n ≥ 0 degrees of freedom has density

f_n(x) = 1 / (2^(n/2) Γ(n/2)) x^(n/2-1) e^(-x/2)

for x > 0. The mean and variance are n and 2n.

The non-central chi-squared distribution with `df`= n degrees of freedom and non-centrality parameter `ncp` = λ has density

f(x) = exp(-λ/2) SUM_{r=0}^∞ ((λ/2)^r / r!) dchisq(x, df + 2r)

for x ≥ 0. For integer n, this is the distribution of the sum of squares of n normals each with variance one, λ being the sum of squares of the normal means; further,
E(X) = n + λ, Var(X) = 2(n + 2*λ), and E((X - E(X))^3) = 8(n + 3*λ).

Note that the degrees of freedom `df`= n, can be non-integer, and also n = 0 which is relevant for non-centrality λ > 0, see Johnson et al (1995, chapter 29). In that (noncentral, zero df) case, the distribution is a mixture of a point mass at x = 0 (of size `pchisq(0, df=0, ncp=ncp)` and a continuous part, and `dchisq()` is not a density with respect to that mixture measure but rather the limit of the density for df -> 0.

Note that `ncp` values larger than about 1e5 may give inaccurate results with many warnings for `pchisq` and `qchisq`.

### Value

`dchisq` gives the density, `pchisq` gives the distribution function, `qchisq` gives the quantile function, and `rchisq` generates random deviates.

Invalid arguments will result in return value `NaN`, with a warning.

The length of the result is determined by `n` for `rchisq`, and is the maximum of the lengths of the numerical arguments for the other functions.

The numerical arguments other than `n` are recycled to the length of the result. Only the first elements of the logical arguments are used.

### Note

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.

### Source

The central cases are computed via the gamma distribution.

The non-central `dchisq` and `rchisq` are computed as a Poisson mixture central of chi-squares (Johnson et al, 1995, p.436).

The non-central `pchisq` is for `ncp < 80` computed from the Poisson mixture of central chi-squares and for larger `ncp` via a C translation of

Ding, C. G. (1992) Algorithm AS275: Computing the non-central chi-squared distribution function. Appl.Statist., 41 478–482.

which 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).

The non-central `qchisq` is based on inversion of `pchisq`.

### References

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, chapters 18 (volume 1) and 29 (volume 2). Wiley, New York.

### See Also

Distributions for other standard distributions.

A central chi-squared distribution with n degrees of freedom is the same as a Gamma distribution with `shape` a = n/2 and `scale` s = 2. Hence, see `dgamma` for the Gamma distribution.

### Examples

    require(graphics)

    dchisq(1, df = 1:3)
    pchisq(1, df =  3)
    pchisq(1, df =  3, ncp = 0:4)  # includes the above

    x <- 1:10
    ## Chi-squared(df = 2) is a special exponential distribution
    all.equal(dchisq(x, df = 2), dexp(x, 1/2))
    all.equal(pchisq(x, df = 2), pexp(x, 1/2))

    ## non-central RNG -- df = 0 with ncp > 0:  Z0 has point mass at 0!
    Z0 <- rchisq(100, df = 0, ncp = 2.)
    graphics::stem(Z0)

    ## visual testing
    ## do P-P plots for 1000 points at various degrees of freedom
    L <- 1.2; n <- 1000; pp <- ppoints(n)
    op <- par(mfrow = c(3,3), mar = c(3,3,1,1)+.1, mgp = c(1.5,.6,0),
              oma = c(0,0,3,0))
    for(df in 2^(4*rnorm(9))) {
      plot(pp, sort(pchisq(rr <- rchisq(n, df = df, ncp = L), df = df, ncp = L)),
           ylab = "pchisq(rchisq(.),.)", pch = ".")
      mtext(paste("df = ", formatC(df, digits = 4)), line =  -2, adj = 0.05)
      abline(0, 1, col = 2)
    }
    mtext(expression("P-P plots : Noncentral  "*
                     chi^2 *"(n=1000, df=X, ncp= 1.2)"),
          cex = 1.5, font = 2, outer = TRUE)
    par(op)

    ## "analytical" test
    lam <- seq(0, 100, by = .25)
    p00 <- pchisq(0,      df = 0, ncp = lam)
    p.0 <- pchisq(1e-300, df = 0, ncp = lam)
    stopifnot(all.equal(p00, exp(-lam/2)),
              all.equal(p.0, exp(-lam/2)))

[Package stats version 3.3.2 Index]