sampling (version 2.1)

strata: 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 appear 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,

pik

vector of selection probabilities or auxiliary information used to compute them; this argument is only used for unequal probability sampling (Poisson, systematic). If an auxiliary information is provided, the function uses the

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.

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 final unit inclusion probability.
• Replicates
  if the method is "srswr", the number of unit replicates is also given.

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)
############
## 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$income)
# extracts the observed data
getdata(data,s)
############
## Example 3
############
# Uses the 'swissmunicipalities' data for drawing a sample of units
data(swissmunicipalities)
# the variable 'REG' has 7 categories in the population
# it is used as stratification variable
# Computes the population stratum sizes
table(swissmunicipalities$REG)
# do not run
#  1   2   3   4   5   6   7 
# 589 913 321 171 471 186 245 
# the sample stratum sizes are given by size=c(30,20,45,15,20,11,44)
# the method is simple random sampling without replacement 
# (equal probability, without replacement)
st=strata(swissmunicipalities,stratanames=c("REG"),size=c(30,20,45,15,20,11,44), 
method="srswor")
# extracts the observed data
# the order of the columns is different from the order in the swsissmunicipalities database
getdata(swissmunicipalities, st)