RSSampling (version 1.0)

meanRSS: Mean estimation based on ranked set sampling

Description

The meanRSS function estimates the population mean based on ranked set sampling. Also, it calculates confidence interval, p-value and z-statistics for hypothesis testing.

Usage

meanRSS(X,m,r,alpha=0.05,alternative="two.sided",mu_0)

Arguments

X

is an obtained ranked set sample

m

is the size of units in each set

r

is the number of cycles

alpha

is the alpha value for the confidence interval. (By default = 0.05)

alternative

is a character string, one of "greater","less" or "two.sided". For one sample test, alternative refers to the true mean of the parent population in relation to the hypothesized value mu_0

mu_0

is the initial value for mean in hypothesis testing formula

Value

mean

the estimated population mean based on ranked set sampling

CI

is a confidence interval for the true mean

z.test

the z-statistic for the test

p.value

the p-value for the test

Details

An obtained ranked set sample X must be m by r matrix.

References

Chen, Z., Bai Z., Sinha B. K. (2003). Ranked Set Sampling: Theory and Application. New York: Springer.

Examples

# NOT RUN {
library("LearnBayes")
mu=c(1,12,2)
Sigma <- matrix(c(1,2,0,2,5,0.5,0,0.5,3), 3, 3)
x <- rmnorm(10000, mu, Sigma)
xx=as.numeric(x[,1])
xy=as.numeric(x[,2])
samplerss=con.Mrss(xx,xy,m=4,r=8,type="r",sets=FALSE,concomitant=FALSE)$sample.x

## mean estimation, confidence interval and hypothesis testing for ranked set sample
meanRSS(samplerss,m=4,r=8,mu_0=1)

# }