pchisqsum {survey} R Documentation

### Description

The distribution of a quadratic form in p standard Normal variables is a linear combination of p chi-squared distributions with 1df. When there is uncertainty about the variance, a reasonable model for the distribution is a linear combination of F distributions with the same denominator.

### Usage

pchisqsum(x, df, a, lower.tail = TRUE,
pFsum(x, df, a, ddf=Inf,lower.tail = TRUE,


### Arguments

 x Observed values df Vector of degrees of freedom a Vector of coefficients ddf Denominator degrees of freedom lower.tail lower or upper tail? method See Details below ... arguments to pchisqsum

### Details

The "satterthwaite" method uses Satterthwaite's approximation, and this is also used as a fallback for the other methods. The accuracy is usually good, but is more variable depending on a than the other methods and is anticonservative in the right tail (eg for upper tail probabilities less than 10^-5). The Satterthwaite approximation requires all a>0.

"integration" requires the CompQuadForm package. For pchisqsum it uses Farebrother's algorithm if all a>0. For pFsum or when some a<0 it inverts the characteristic function using the algorithm of Davies (1980). These algorithms are highly accurate for the lower tail probability, but they obtain the upper tail probability by subtraction from 1 and so fail completely when the upper tail probability is comparable to machine epsilon or smaller.

If the CompQuadForm package is not present, a warning is given and the saddlepoint approximation is used.

"saddlepoint" uses Kuonen's saddlepoint approximation. This is moderately accurate even very far out in the upper tail or with some a=0 and does not require any additional packages. The relative error in the right tail is uniformly bounded for all x and decreases as p increases. This method is implemented in pure R and so is slower than the "integration" method.

The distribution in pFsum is standardised so that a likelihood ratio test can use the same x value as in pchisqsum. That is, the linear combination of chi-squareds is multiplied by ddf and then divided by an independent chi-squared with ddf degrees of freedom.

### Value

Vector of cumulative probabilities

### References

Chen, T., & Lumley T. (2019). Numerical evaluation of methods approximating the distribution of a large quadratic form in normal variables. Computational Statistics and Data Analysis, 139, 75-81.

Davies RB (1973). "Numerical inversion of a characteristic function" Biometrika 60:415-7

Davies RB (1980) "Algorithm AS 155: The Distribution of a Linear Combination of chi-squared Random Variables" Applied Statistics,Vol. 29, No. 3 (1980), pp. 323-333

P. Duchesne, P. Lafaye de Micheaux (2010) "Computing the distribution of quadratic forms: Further comparisons between the Liu-Tang-Zhang approximation and exact methods", Computational Statistics and Data Analysis, Volume 54, (2010), 858-862

Farebrother R.W. (1984) "Algorithm AS 204: The distribution of a Positive Linear Combination of chi-squared random variables". Applied Statistics Vol. 33, No. 3 (1984), p. 332-339

Kuonen D (1999) Saddlepoint Approximations for Distributions of Quadratic Forms in Normal Variables. Biometrika, Vol. 86, No. 4 (Dec., 1999), pp. 929-935

pchisq

### Examples

x <- 2.7*rnorm(1001)^2+rnorm(1001)^2+0.3*rnorm(1001)^2
x.thin<-sort(x)[1+(0:50)*20]
p.invert<-pchisqsum(x.thin,df=c(1,1,1),a=c(2.7,1,.3),method="int" ,lower=FALSE)
p.satt<-pchisqsum(x.thin,df=c(1,1,1),a=c(2.7,1,.3),method="satt",lower=FALSE)