# Chisquare

0th

Percentile

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

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.

Keywords
distribution
##### 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 $P[X \le x]$, otherwise, $P[X > x]$.

##### Details

The chi-squared distribution with df$= n \ge 0$ degrees of freedom has density $$f_n(x) = \frac{1}{{2}^{n/2} \Gamma (n/2)} {x}^{n/2-1} {e}^{-x/2}$$ 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 $= \lambda$ has density $$f(x) = e^{-\lambda / 2} \sum_{r=0}^\infty \frac{(\lambda/2)^r}{r!}\, f_{n + 2r}(x)$$ for $x \ge 0$. For integer $n$, this is the distribution of the sum of squares of $n$ normals each with variance one, $\lambda$ being the sum of squares of the normal means; further,

$E(X) = n + \lambda$, $Var(X) = 2(n + 2*\lambda)$, and $E((X - E(X))^3) = 8(n + 3*\lambda)$.

Note that the degrees of freedom df$= n$, can be non-integer, and also $n = 0$ which is relevant for non-centrality $\lambda > 0$, 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 $df \to 0$.

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.

##### 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.

Distributions for other standard distributions.

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

• Chisquare
• dchisq
• pchisq
• qchisq
• rchisq
##### Examples
library(stats) # NOT RUN { 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) # } # 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) # } # NOT RUN { ## "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))) # } 
Documentation reproduced from package stats, version 3.6.1, License: Part of R 3.6.1

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