power.rq.test: Power and sample size for quantile regression.

In statapps/power.ctepd: Power and sample size for clinical trials and epidemiology

Power and sample size for quantile regression.

Description

Compute power and sample size of test under alternative hypothesis to obtain target power (same as power.anova.test)

Usage

     power.rq.test(n=NULL, x=NULL, sig.level=0.05,power=NULL, tau = 0.5,
                     beta=1, sd=1, dist="Norm", kernel.smooth=NULL, bw=NULL,
                     alternative = c("two.sided", "one.sided"))

Arguments

n	Given sample size to compute power.
x	A rqfun object.See "Details" and "Examples".
sig.level	Significance level of the test. The default is 0.05.
power	Power of the test between 0 and 1. It is required to compute sample size.
tau	The desired quantile between 0 and 1. The default is 0.5.
beta	The desired quantile regression coefficient. It can be a sigle number or a vector.
sd	Standard deviation or scale of error distribution.
dist	The distribution of error. It takes "Norm","Cauchy","Gamma" or a vector of residuals if distribution is unknown and to be estimated.
kernel.smooth	It takes values 1,2,3,4 indicating the number of kernels used. 1 kernel is a uniform variable bounded by -1/2 and 1/2. Standard normal kernel is used by default if no values are specified.
bw	The bandwidth used in kernel smoothing. Small bandwidth results in under-smoothing whereas large bandwidth causes over-smoothing. Default is NULL and bandwidth is estimated by normal distribution approximation or Silverman's (1986) rule of thumb by default.
alternative	Choose either "one.sided" for one sided test or "two.sided" for two sided test.

Details

A rqfun object needs to be defined in univariate regression situation. Here "univariate" means the independent variables are transformation of a single variable. It is defined as following: x = rqfun(mu=0, sd=1, dist='norm', term=c('1'), pos=2, method='exact', a =NA, b=NA). It contains the information of the independent variable.

It is assumed that error distribution is iid, so the calculation of variance of regression coefficients are based on Section 3.2.2 of Quantile Regression(2005).

Note

Either n or power needs to be defined. They can not be NULL at the same time. If estimated sample size is desired, power should be given; if estimated power is desired, actual sample size n should be given.

References

Roger Koenker. (2005)Quantile Regression. Cambridge University Press. Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. London: Chapman & Hall/CRC.

Examples

 #Construct x
 x = rqfun(mu = 5, sd = 1.5, dist = "norm", pos = 2, term = c('1'),a=NA,b=NA, method = 'exact')
 #Compute estimated sample size when power is 0.8
 power.rq.test(x=x, power = 0.8, tau = 0.2, beta = c(1,0.5),
                sd = 10, dist = "Norm", alternative = 'two.sided')
 

statapps/power.ctepd documentation built on April 26, 2024, 3:22 a.m.