dr.pvalue: Compute the Chi-square approximations to a weighted sum of...
In dr: Methods for Dimension Reduction for Regression

Description Usage Arguments Details Value Author(s) References

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

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

bentlerxie.pvalue(coef, f)

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

`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.

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.

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.

Sanford Weisberg <sandy@stat.umn.edu>

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, 8:49 a.m.