# 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. ```r 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 ## See Also `S.SI, E.SI` ## Returns 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 ## Author(s) Hugo Andres Gutierrez Rojas hagutierrezro@gmail.com ## 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. ## Examples ```r ############ ## 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) ```

T.SIC.rd function