Chisquare {stats} | R Documentation |

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`

.

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)

`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]. |

The chi-squared distribution with `df`

*= n > 0* degrees of
freedom has density

*f_n(x) = 1 / (2^(n/2) Gamma(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(-lambda/2) SUM_{r=0}^infty ((lambda/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 for non-centrality *λ > 0*, even *n = 0*;
see Johnson et al. (1995, chapter 29).

Note that `ncp`

values larger than about 1e5 may give inaccurate
results with many warnings for `pchisq`

and `qchisq`

.

`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 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`

based on 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).

The non-central `qchisq`

is based on inversion of `pchisq`

.

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.

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.

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 is ok for ncp > 0: Z0 has point mass at 0! Z0 <- rchisq(100, df = 0, ncp = 2.) graphics::stem(Z0) ## Not run: ## 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) ## End(Not run)

[Package *stats* version 2.5.0 Index]