meanR-help: Mean estimation based on ranked set sampling
In RSSampling: Ranked Set Sampling

Description Usage Arguments Details Value References Examples

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.

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

`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

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

`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

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

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)