n.ESTMAS: Sample Size Through Stratified Sampling

In ProbSamplingI: Probabilistic Sampling Design and Strategies

Sample Size Through Stratified Sampling

Description

The n.ESTMAS function determines the sample size with its corresponding allocation by stratum, using a stratified sampling strategy, where a simple random sampling design with no replacement (ESTMAS) is applied in each stratum; taking into account whether the parameter of interest is the average (or total) or a proportion.

Usage

n.ESTMAS(Nh,Sh,Ch,Ph,Emax.a,Nc=0.95,parameter="mean",Asig="Optima")

# n.ESTMAS(Nh,Sh,Ch,Emax.a,Nc=0.95,parameter="mean",Asig="Optima")
# n.ESTMAS(Nh,Ph,Ch,Emax.a,Nc=0.95,parameter="prop",Asig="Optima")

# n.ESTMAS(Nh,Sh,Emax.a,Nc=0.95,parameter="mean",Asig="Neyman")
# n.ESTMAS(Nh,Ph,Emax.a,Nc=0.95,parameter="prop",Asig="Neyman")

# n.ESTMAS(Nh,Sh,Emax.a,Nc=0.95,parameter="mean",Asig="Proportional")
# n.ESTMAS(Nh,Ph,Emax.a,Nc=0.95,parameter="prop",Asig="Proportional")

Arguments

Nh	Numerical vector with the respective sizes of strata.
Sh	Numerical vector with the respective standard deviations of the variable of interest of each stratum. This argument is necessary only if the parameter of interest is the mean.
Ch	Numerical vector with the costs of sampling an element within each stratum. This argument is only necessary if the allocation by stratum is the optimal allocation.
Ph	Numerical vector with estimated proportions within each stratum.
Emax.a	Absolute maximum error.
parameter	Type of parameter to be estimated, either the mean or a proportion ("mean", "prop").
Nc	Confidence level (between 0 and 1) that you want to set.
Asig	Assignment by stratum ("Optima", "Neyman" or "Proportional")

Value

This function returns the sample size and the allocation by stratum, through the conditions established in the arguments.

Author(s)

Jorge Alberto Barón Cárdenas <[e-mail]>

Guillermo Martínez Flórez <[e-mail]>

References

Särndal, C. E., J. H. Wretman, and C. M. Cassel (1992). Foundations of Inference in Survey Sampling. Wiley New York.

Cochran, W. G. (1977). Sampling Techniques, 3ra ed. New York: Wiley.

Thompson, S. K. (1945). Wiley Series in Probability and Statistics, Sampling, 1ra ed. United States of America.

Examples

Nc<-0.95
E<-0.3
Nh<-c(400,220,380)
Sh<-sqrt(c(0.7521,1.4366,1.1361))
Ph<-c(0.4,0.2,0.6)
Ch<-c(1000,1200,1500)

# Optimal Assignment
n.ESTMAS(Nh=Nh,Sh=Sh,Ch=Ch,E=E,Nc=0.95,parameter="mean",Asig="Optima")
n.ESTMAS(Nh=Nh,Ph=Ph,Ch=Ch,E=E,Nc=0.95,parameter="prop",Asig="Optima")

# Neyman Assignment
n.ESTMAS(Nh=Nh,Sh=Sh,E=E,Nc=0.95,parameter="mean",Asig="Neyman")
n.ESTMAS(Nh=Nh,Ph=Ph,E=E,Nc=0.95,parameter="prop",Asig="Neyman")

# Proportional Assignment
n.ESTMAS(Nh=Nh,Sh=Sh,E=E,Nc=0.95,parameter="mean",Asig="Proportional")
n.ESTMAS(Nh=Nh,Ph=Ph,E=E,Nc=0.95,parameter="prop",Asig="Proportional")

ProbSamplingI documentation built on May 29, 2024, 6:15 a.m.