# 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

 ```1 2 3 4 5``` ```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 <sandy@stat.umn.edu>

## 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, 8:49 a.m.