cluster.samplesize {samplingR}

Sample size estimation on cluster sampling

Description

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

Usage

cluster.samplesize(
  N,
  data,
  error,
  alpha,
  estimator = c("total", "mean", "proportion", "class total"),
  replace = FALSE
)

Arguments

N

Number of clusters in the population.

data

Dataset.

error

Sampling error.

alpha

Significance level to obtain confidence intervals.

estimator

The estimator to be estimated. Default is "total".

replace

Whether the samples to be taken can have repeated instances or not.

Details

This function admits both grouped and non-grouped by cluster data.
Non-grouped data must have interest variable data in the first column and cluster name each individual belongs to in the last column.
Grouped by cluster data must have interest variable data in the first column, cluster size in the second and the cluster name in the last column. Interest values of grouped data must reflect the total value of each cluster.

Value

Number of clusters to be taken.

Examples

d<-cbind(rnorm(500, 50, 20), rep(c(1:50),10)) #Non-grouped data
sample<-cluster.sample(d, n=10) #Non-grouped sample
sampleg<-aggregate(sample[,1], by=list(Category=sample[,2]), FUN=sum)
sampleg<-cbind(sampleg[,2], rep(10,10), sampleg[,1]) #Sample sample with grouped data

#Cluster size to be taken for estimation
cluster.samplesize(N=50, data=sample, error=500, estimator="total", replace=TRUE)

newsample<-cluster.sample(d, n=26) #New sample for estimation
sum(d[,1])
cluster.estimator(N=50, data=newsample, estimator="total", alpha=0.05, replace=TRUE)
cluster.estimator(N=50, data=sampleg, estimator="total", alpha=0.05)