dr.pvalue: Compute the Chi-square approximations to a weighted sum of...

In dr: Methods for Dimension Reduction for Regression

Description

Returns an approximate quantile for a weighted sum of independent χ^2(1) random variables.

Usage

dr.pvalue(coef,f,chi2approx=c("bx","wood"),...)

bentlerxie.pvalue(coef, f)

wood.pvalue(coef, f, tol=0.0, print=FALSE)

Arguments

coef	a vector of nonnegative weights
f	Observed value of the statistic
chi2approx	Which approximation should be used?
tol	tolerance for Wood's method.
print	Printed output for Wood's method
...	Arguments passed from dr.pvalue to wood.pvalue.

Details

For Bentler-Xie, we approximate f by c χ^2(d) for values of c and d computed by the function. The Wood approximation is more complicated.

Value

Returns a data.frame with four named components:

test	The input argument f.
test.adj	For Bentler-Xie, returns cf; for Wood, returns NA.
df.adj	For Bentler-Xie, returns d; for Wood, returns NA.
pval.adj	Approximate p.value.

Author(s)

Sanford Weisberg <[e-mail]>

References

Peter M. Bentler and Jun Xie (2000), Corrections to test statistics in principal Hessian directions. Statistics and Probability Letters, 47, 381-389.

Wood, Andrew T. A. (1989) An F approximation to the distribution of a linear combination of chi-squared variables. Communications in Statistics: Simulation and Computation, 18, 1439-1456.

dr documentation built on May 2, 2019, 5:49 p.m.