E.STPPS: Estimation of the Population Total under Stratified...

In TeachingSampling: Selection of Samples and Parameter Estimation in Finite Population

Description

Computes the Hansen-Hurwitz estimator of the population total according to a probability proportional to size sampling with replacement design

Usage

E.STPPS(y, pk, mh, S)

Arguments

y	Vector, matrix or data frame containing the recollected information of the variables of interest for every unit in the selected sample
pk	A vector containing selection probabilities for each unit in the sample
mh	Vector of sample size in each stratum
S	Vector identifying the membership to the strata of each unit in selected sample

Details

Returns the estimation of the population total of every single variable of interest, its estimated standard error and its estimated coefficient of variation in all of the stratum and finally in the entire population

Value

The function returns an array composed by several matrices representing each variable of interest. The columns of each matrix correspond to the estimated parameters of the variables of interest in each stratum and in the entire population

Author(s)

Hugo Andres Gutierrez Rojas [e-mail]

References

Sarndal, C-E. and Swensson, B. and Wretman, J. (1992), Model Assisted Survey Sampling. Springer.
Gutierrez, H. A. (2009), Estrategias de muestreo: Diseno de encuestas y estimacion de parametros. Editorial Universidad Santo Tomas.

See Also

S.STPPS

Examples

# Uses the Lucy data to draw a stratified random sample 
# according to a PPS design in each stratum

data(Lucy)
attach(Lucy)
# Level is the stratifying variable
summary(Level)
# Defines the sample size at each stratum
m1<-83
m2<-100
m3<-200
mh<-c(m1,m2,m3)
# Draws a stratified sample
res<-S.STPPS(Level, Income, mh)
# The selected sample
sam<-res[,1]
# The selection probability of each unit in the selected sample
pk <- res[,2]
pk
# The information about the units in the sample is stored in an object called data
data <- Lucy[sam,]
attach(data)
names(data)
# The variables of interest are: Income, Employees and Taxes
# This information is stored in a data frame called estima
estima <- data.frame(Income, Employees, Taxes)
E.STPPS(estima,pk,mh,Level)

TeachingSampling documentation built on April 22, 2020, 1:05 a.m.