Computation of Population Totals for Clusters
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.
T.SIC(y,Cluster)
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 |
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
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.
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## Example 1
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# 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)
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## Example 2 Sampling and estimation in Cluster smapling
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# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.