strata: Stratified sampling

In sampling: Survey Sampling

Stratified sampling

Description

Stratified sampling with equal/unequal probabilities.

Usage

strata(data, stratanames=NULL, size, method=c("srswor","srswr","poisson",
"systematic"), pik,description=FALSE)

Arguments

data	data frame or data matrix; its number of rows is N, the population size.
stratanames	vector of stratification variables.
size	vector of stratum sample sizes (in the order in which the strata are given in the input data set).
method	method to select units; the following methods are implemented: simple random sampling without replacement (srswor), simple random sampling with replacement (srswr), Poisson sampling (poisson), systematic sampling (systematic); if "method" is missing, the default method is "srswor".
pik	vector of inclusion probabilities or auxiliary information used to compute them; this argument is only used for unequal probability sampling (Poisson and systematic). If an auxiliary information is provided, the function uses the inclusionprobabilities function for computing these probabilities.
description	a message is printed if its value is TRUE; the message gives the number of selected units and the number of the units in the population. By default, the value is FALSE.

Details

The data should be sorted in ascending order by the columns given in the stratanames argument before applying the function. Use, for example, data[order(data$state,data$region),].

Value

The function produces an object, which contains the following information:

ID_unit	the identifier of the selected units.
Stratum	the unit stratum.
Prob	the unit inclusion probability.

See Also

getdata, mstage

Examples

############
## Example 1
############
# Example from An and Watts (New SAS procedures for Analysis of Sample Survey Data)
# generates artificial data (a 235X3 matrix with 3 columns: state, region, income).
# the variable "state" has 2 categories ('nc' and 'sc'). 
# the variable "region" has 3 categories (1, 2 and 3).
# the sampling frame is stratified by region within state.
# the income variable is randomly generated
data=rbind(matrix(rep("nc",165),165,1,byrow=TRUE),matrix(rep("sc",70),70,1,byrow=TRUE))
data=cbind.data.frame(data,c(rep(1,100), rep(2,50), rep(3,15), rep(1,30),rep(2,40)),
1000*runif(235))
names(data)=c("state","region","income")
# computes the population stratum sizes
table(data$region,data$state)
# not run
#     nc  sc
#  1 100  30
#  2  50  40
#  3  15   0
# there are 5 cells with non-zero values
# one draws 5 samples (1 sample in each stratum)
# the sample stratum sizes are 10,5,10,4,6, respectively
# the method is 'srswor' (equal probability, without replacement)
s=strata(data,c("region","state"),size=c(10,5,10,4,6), method="srswor")
# extracts the observed data
getdata(data,s)
# see the result using a contigency table
table(s$region,s$state)
############
## Example 2
############
# The same data as in Example 1
# the method is 'systematic' (unequal probability, without replacement)
# the selection probabilities are computed using the variable 'income'
s=strata(data,c("region","state"),size=c(10,5,10,4,6), method="systematic",pik=data$income)
# extracts the observed data
getdata(data,s)
# see the result using a contigency table
table(s$region,s$state)
############
## Example 3
############
# Uses the 'swissmunicipalities' data as population for drawing a sample of units
data(swissmunicipalities)
# the variable 'REG' has 7 categories in the population
# it is used as stratification variable
# Computes the population stratum sizes
table(swissmunicipalities$REG)
# do not run
#  1   2   3   4   5   6   7 
# 589 913 321 171 471 186 245 
# sort the data to obtain the same order of the regions in the sample
data=swissmunicipalities
data=data[order(data$REG),]
# the sample stratum sizes are given by size=c(30,20,45,15,20,11,44)
# 30 units are drawn in the first stratum, 20 in the second one, etc.
# the method is simple random sampling without replacement 
# (equal probability, without replacement)
st=strata(data,stratanames=c("REG"),size=c(30,20,45,15,20,11,44), method="srswor")
# extracts the observed data
getdata(data, st)
# see the result using a contingency table
table(st$REG)

sampling documentation built on Nov. 2, 2023, 6:26 p.m.