strata.samplesize: Sample size estimation on stratified sampling

In samplingR: Sampling and Estimation Methods

Sample size estimation on stratified sampling

Description

Calculates the required sample size in order to achieve an absolute or relative sampling error less or equal to the specified for an specific estimator and an optional confidence interval in stratified sampling.

Usage

strata.samplesize(
  Nh,
  var,
  error,
  alpha,
  estimator = c("total", "mean", "proportion", "class total"),
  alloc = c("prop", "min", "optim"),
  ch,
  p,
  mean,
  replace = FALSE,
  relative = FALSE
)

Arguments

Nh	Vector of population strata sizes.
var	Vector of estimated strata variances.
error	Sampling error.
alpha	Significance level to obtain confidence intervals.
estimator	The estimator to be estimated. Default is "total".
alloc	The allocation to be used when taking samples. Default is "prop".
ch	Vector of cost per strata to select an individual for the sample.
p	Estimated population proportion. If estimator is not "proportion" or "class total" it will be ignored.
mean	Estimated population mean. If relative=FALSE it will be ignored.
replace	Whether the samples to be taken can have repeated instances or not.
relative	Whether the specified error is relative or not.

Details

With "proportion" and "class total" estimators variance vector must contain var return values equal to \frac{Nh}{(Nh-1)}p*(1-p) values.

Value

Number of instances of the sample to be taken.

Examples

strata.samplesize(c(120,100,110,50), c(458, 313,407,364), error=5, alpha=0.05, "mean", "prop")

samplingR documentation built on July 9, 2023, 7:26 p.m.