TeachingSampling (version 4.1.1)

T.SIC: Computation of Population Totals for Clusters

Description

Computes the population total of the characteristics of interest in clusters. This function is used in order to estimate totals when doing a Pure Cluster Sample.

Usage

T.SIC(y,Cluster)

Arguments

y

Vector, matrix or data frame containing the recollected information of the variables of interest for every unit in the selected sample

Cluster

Vector identifying the membership to the cluster of each unit in the selected sample of clusters

Value

The function returns a matrix of clusters totals. The columns of each matrix correspond to the totals of the variables of interest in each cluster

References

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.

See Also

S.SI, E.SI

Examples

# NOT RUN {
############
## Example 1
############
# Vector U contains the label of a population of size N=5
U <- c("Yves", "Ken", "Erik", "Sharon", "Leslie")
# Vector y1 and y2 are the values of the variables of interest
y1<-c(32, 34, 46, 89, 35)
y2<-c(1,1,1,0,0)
y3<-cbind(y1,y2)
# Vector Cluster contains a indicator variable of cluster membership 
Cluster <- c("C1", "C2", "C1", "C2", "C1")
Cluster
# Draws a stratified simple random sample without replacement of size n=3
T.SIC(y1,Cluster)
T.SIC(y2,Cluster)
T.SIC(y3,Cluster)

########################################################
## Example 2 Sampling and estimation in Cluster smapling
########################################################
# Uses Lucy data to draw a clusters sample according to a SI design
# Zone is the clustering variable
data(Lucy)
attach(Lucy)
summary(Zone)
# The population of clusters
UI<-c("A","B","C","D","E")
NI=length(UI)
# The sample size
nI=2
# Draws a simple random sample of two clusters
samI<-S.SI(NI,nI)
dataI<-UI[samI]
dataI   
# The information about each unit in the cluster is saved in Lucy1 and Lucy2
data(Lucy)
Lucy1<-Lucy[which(Zone==dataI[1]),]
Lucy2<-Lucy[which(Zone==dataI[2]),]
LucyI<-rbind(Lucy1,Lucy2)
attach(LucyI)
# The clustering variable is Zone
Cluster <- as.factor(as.integer(Zone))
# The variables of interest are: Income, Employees and Taxes
# This information is stored in a data frame called estima
estima <- data.frame(Income, Employees, Taxes)
Ty<-T.SIC(estima,Cluster)
# Estimation of the Population total
E.SI(NI,nI,Ty)
# }