S.STpiPS: Stratified Sampling Applying Without Replacement piPS Design...
In psirusteam/TeachingSampling: Selection of Samples and Parameter Estimation in Finite Population

Description Usage Arguments Details Value Author(s) References See Also Examples

Draws a probability proportional to size simple random sample without replacement of size n_h in stratum h of size N_h

1	S.STpiPS(S,x,nh)

`S`	Vector identifying the membership to the strata of each unit in the population
`x`	Vector of auxiliary information for each unit in the population
`nh`	Vector of sample size in each stratum

The selected sample is drawn according to the Sunter method (sequential-list procedure) in each stratum

The function returns a matrix of n=n_1+\cdots+n_h rows and two columns. Each element of the first column indicates the unit that was selected. Each element of the second column indicates the inclusion probability of this unit

Hugo Andres Gutierrez Rojas hagutierrezro@gmail.com

Sarndal, C-E. and Swensson, B. and Wretman, J. (1992), Model Assisted Survey Sampling. Springer.
Gutierrez, H. A. (2009), Estrategias de muestreo: Diseno de encuestas y estimacion de parametros. Editorial Universidad Santo Tomas.

E.STpiPS

############
## Example 1
############
# Vector U contains the label of a population of size N=5
U <- c("Yves", "Ken", "Erik", "Sharon", "Leslie")
# The auxiliary information
x <- c(52, 60, 75, 100, 50)
# Vector Strata contains an indicator variable of stratum membership 
Strata <- c("A", "A", "A", "B", "B")
# Then sample size in each stratum
mh <- c(2,2)
# Draws a stratified PPS sample with replacement of size n=4
res <- S.STPPS(Strata, x, mh)
# The selected sample
sam <- res[,1]
U[sam]
# The selection probability of each unit selected to be in the sample
pk <- res[,2]
pk

############
## Example 2
############
# Uses the Lucy data to draw a stratified random sample 
# according to a piPS design in each stratum

data(Lucy)
attach(Lucy)
# Level is the stratifying variable
summary(Level)

# Defines the size of each stratum
N1<-summary(Level)[[1]]
N2<-summary(Level)[[2]]
N3<-summary(Level)[[3]]
N1;N2;N3

# Defines the sample size at each stratum
n1<-70
n2<-100
n3<-200
nh<-c(n1,n2,n3)
nh
# Draws a stratified sample
S <- Level
x <- Employees

res <- S.STpiPS(S, x, nh)
sam<-res[,1]
# The information about the units in the sample is stored in an object called data
data <- Lucy[sam,]
data
dim(data)
# The selection probability of each unit selected in the sample
pik <- res[,2]
pik