plot.finite.sample.dbn: Generates plot for object of class 'samples.population'.

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

Generates plot for object of class 'samples.population'.

Description

This function generates a plot of the samples in an object of class 'samples.population'.

Usage

                   plot.finite.sample.dbn(dat,type="point.est",show.mean=TRUE,freq=TRUE,true.stat=NULL, ...)
                   plot(dat,type="point.est",show.mean=TRUE,freq=TRUE,true.stat=NULL, ...)

Arguments

dat	Object of class 'samples.population'.
type	Type of statistic to plot. Valid types are "point.est"for plot of point estimates in object, "var.est"for plot of variance estimates in object, "ci.est"for plot of confidence interval estimates in object.
freq	If TRUE, the histogram shows frequencies, else it shows density.
show.mean	If TRUE and type=="point.est" or type=="var.est", the mean of the sampling distribution is shown on the histogram.
true.stat	The value of the corresponding statistic in the population. Only used if type=="ci.est".
...	Other arguments to be passed to the function 'hist'.

Details

Plots point estimate, variance estimate or confidence interval estimates of an object of class 'samples.population'. Use to investigate properties of the sampling distribution of the point estimate, variance estimate or confidence interval estimate.

Value

The function creates a plot.

See Also

plot.sample

Examples

                   data(barnett) # get barnett data
                   
                   # All samples total weight of class from srs:
                   samp.dbn<-take.sample(barnett,y.name="weight",n=23,take.all=TRUE)
                   # estimate total
                   samp.dbn<-add.point.est(samp.dbn,type="ybar.tot")
                   samp.dbn<-add.var.est(samp.dbn,type="ybar.tot")
                   samp.dbn<-add.ci.est(samp.dbn)
                   plot(samp.dbn)
                   plot(samp.dbn,type="var.est")
                   plot(samp.dbn,type="ci.est",true.stat=sum(barnett$weight))
                   
                   # proportion of class with weight more than 15, from srs:
                   samp.dbn<-take.sample(barnett,y.name="weight",n=23,take.all=TRUE)
                   # estimate total
                   samp.dbn<-add.point.est(samp.dbn,type="ybar.ppn",cond=">15")
                   samp.dbn<-add.var.est(samp.dbn,type="ybar.ppn",cond=">15")
                   samp.dbn<-add.ci.est(samp.dbn)
                   plot(samp.dbn)
                   plot(samp.dbn,type="var.est")
                   plot(samp.dbn,type="ci.est",true.stat=
                   (sum(barnett$weight>15)/length(barnett$weight)))
                   
                   # 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.dbn<-take.sample(strat.barnett,y.name="height",type="strat",
                   n=c(2,9,7,3),take.all=TRUE)
                   # estimate mean height using stratified estimator
                   samp.dbn<-add.point.est(samp.dbn,type="strat.mean")
                   samp.dbn<-add.var.est(samp.dbn,type="strat.mean")
                   samp.dbn<-add.ci.est(samp.dbn)
                   plot(samp.dbn)
                   plot(samp.dbn,type="var.est")
                   plot(samp.dbn,type="ci.est",true.stat=mean(barnett$height))
                   
                   # Cluster mean
                   # Define 5 clusters based on weight:
                   clust.barnett<-define.subunit(barnett,aux.name="weight",
                   breaks=c(0,10,15,20,25,30,40),type="clust")
                   # take cluster sample of size 3
                   samp.dbn<-take.sample(clust.barnett,y.name="height",type="clust",m=3,
                   take.all=TRUE)
                   # estimate mean height using cluster estimator
                   samp.dbn<-add.point.est(samp.dbn,type="clust.mean")
                   samp.dbn<-add.var.est(samp.dbn,type="clust.mean")
                   samp.dbn<-add.ci.est(samp.dbn)
                   plot(samp.dbn)
                   plot(samp.dbn,type="var.est")
                   plot(samp.dbn,type="ci.est",true.stat=mean(barnett$height))
                   
                   # Ratio estimator
                   # take srs of size 6, with weight as auxiliary variable:
                   samp.dbn<-take.sample(barnett,y.name="height",aux.name="weight",type="srs",
                   n=23,take.all=TRUE)
                   # estimate mean height using cluster estimator
                   samp.dbn<-add.point.est(samp.dbn,type="ratio.mean")
                   samp.dbn<-add.var.est(samp.dbn,type="ratio.mean")
                   samp.dbn<-add.ci.est(samp.dbn)
                   plot(samp.dbn)
                   plot(samp.dbn,type="var.est")
                   plot(samp.dbn,type="ci.est",true.stat=mean(barnett$height))
                   
                   

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