E.CI: Confidence Interval for Estimation of the Population Total...

In damarals/TeachingSampling: Selection of Samples and Parameter Estimation in Finite Population

Description

Computes the Confidence Interval (1-alpha) utilizing Normal Distribution of the population total and mean for an specified sampling

Usage

E.CI(N, est, type, level)

Arguments

N	Population size or Vector of Population sizes for each Class in Stratified Sampling (Nh)
est	Estimator of the Population Total
type	type of Confidence Interval: "total" or "mean"
level	level of Interval: (1-alpha)

Details

Returns the confidence interval of estimation of the population total or mean of a sampling design, utilizing the estimated standard error and total

Value

The function returns a data matrix whose columns correspond to the bands of confidence estimated of population total or mean

Author(s)

Daniel de Amaral da Silva [e-mail]

References

Sarndal, C-E. and Swensson, B. and Wretman, J. (1992), Model Assisted Survey Sampling. Springer.

See Also

E.CI

Examples

############
## Example 1
############
# Uses the Lucy data to get a Confidence Interval to total according to a SI design
data(Lucy)
attach(Lucy)

N <- dim(Lucy)[1]
n <- 400
sam <- S.SI(N,n)
# The information about the units in the sample is stored in an object called data
data <- Lucy[sam,]
attach(data)
names(data)
# 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)
est <- E.SI(N,n,estima)
# Passing the informations to calculate the Confidence Interval to total
E.CI(N, est, type = 'total', level = 0.95)

############
## Example 2
############
# Uses the Lucy data to get a Confidence Interval to mean according to a Bernoulli sample
data(Lucy)
attach(Lucy)

N <- dim(Lucy)[1]
n=400
prob=n/N
sam <- S.BE(N,prob)
# The information about the units in the sample is stored in an object called data
data <- Lucy[sam,]
attach(data)
names(data)
# 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)
est <- E.BE(estima,prob)
# Passing the informations to calculate the Confidence Interval to mean
E.CI(N, est, type = 'mean', level = 0.95)

############
## Example 3
############
# Uses the Lucy data to get a Confidence Interval to mean according to a Stratified Sampling piPS
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]]
Nh = c(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]
pik <- res[,2]
data <- Lucy[sam,]
attach(data)
estima <- data.frame(Income, Employees, Taxes)
est <- E.STpiPS(estima,pik,Level) 
# Passing the informations to calculate the Confidence Interval with level 0.98 to mean
E.CI(Nh, est, type = 'mean', level = 0.98)
  

damarals/TeachingSampling documentation built on June 2, 2019, 9:06 p.m.