ci.est: Calculates confidence interval estimates from samples.

In david-borchers/sampling: Module MT4608 Sampling Theory Practical Package

Calculates confidence interval estimates from samples.

Description

This function calculates confidence interval estimates of population mean, total or proportion from an object of class 'sample' or 'samples.population'.

Usage

                   ci.est(dat,type="ybar",level=0.95)

Arguments

dat	Object of class 'sample' or 'samples.population'.
type	Type of confidence interval estimate. Valid types are ybarsample mean ybar.totsample total ybar.ppnsample proportion strat.meanstratified sample mean strat.totstratified sample totatl strat.ppnstratified sample proportion ratio.meanratio estimate of mean ratio.totratio estimate of toatal ratio.ppnratio estimate of proportion strat.ratio.meanstratified ratio estimate of mean strat.ratio.totstratified ratio estimate of total strat.ratio.ppnstratified ratio estimate of proportion regr.meanregression estimate of mean regr.totregression estimate of total regr.ppnregression estimate of proportion strat.regr.meanstratified regression estimate of mean strat.regr.totstratified regression estimate of total strat.regr.ppnstratified regression estimate of proportion diffr.meandifference estimate of mean diffr.totdifference estimate of total diffr.ppndifference estimate of proportion strat.diffr.meanstratified difference estimate of mean strat.diffr.totstratified difference estimate of total strat.diffr.ppnstratified difference estimate of proportion clust.meancluster estimate of mean from mean of cluster totals clust.totcluster estimate of mean from total of cluster totals clust.ppncluster estimate of proportion from mean of cluster totals clust.r.meancluster ratio estimate of mean clust.r.totcluster ratio estimate of total clust.r.ppncluster ratio estimate of proportion
level	Confidence level; 0.95 (95%) by default.

Details

Returns an approximate confidence interval estimate of the appropriate type, assuming that the statistic has a t-distribution with degrees of freedom equal to sample size.

Value

The function returns a list with elements $ci.lower and $ci.upper, being the lower and upper (100*level) confidence interval bounds from the sample.

See Also

add.ci.est, var.est, add.var.est, point.est, add.point.est

Examples

                   data(barnett) # get barnett data
                   
                   # total weight of class from srs:
                   samp<-take.sample(barnett,y.name="weight",n=6)
                   # estimate total
                   ci.est(samp,type="ybar.tot")
                   
                   # proportion of class with weight more than 15 from srs:
                   samp<-take.sample(barnett,y.name="weight",n=6)
                   # estimate total
                   ci.est(samp,type="ybar.ppn",cond=">15")
                   
                   # Stratified mean
                   # Define 4 strata based on weight, with 10 lb interval widths:
                   strat.barnett<-define.subunit(barnett,aux.name="weight",breaks=c(0,10,20,30,40),type="strat")
                   # take stratified sample
                   samp<-take.sample(strat.barnett,y.name="height",type="strat",n=c(2,5,4,2))
                   # estimate mean height using stratified estimator
                   ci.est(samp,type="strat.mean")
                   
                   # Cluster mean
                   # Define 4 clusters based on weight, with 10 lb interval widths:
                   clust.barnett<-define.subunit(barnett,aux.name="weight",breaks=c(0,10,20,30,40),type="clust")
                   # take cluster sample of size 2
                   samp<-take.sample(clust.barnett,y.name="height",type="clust",m=2)
                   # estimate mean height using cluster estimator
                   ci.est(samp,type="clust.mean")
                   
                   # Ratio estimator
                   # take srs of size 6, with weight as auxiliary variable:
                   samp<-take.sample(clust.barnett,y.name="height",aux.name="weight",type="srs",n=6)
                   # estimate mean height using cluster estimator
                   ci.est(samp,type="ratio.mean")
                   
                   
                   

david-borchers/sampling documentation built on Sept. 17, 2022, 7:54 a.m.