submean {samplingbook} | R Documentation |
Sub-sample Mean Estimation
Description
The function submean
estimates the population mean out of sub-samples (two-stage samples) either with or without consideration of finite population correction in both stages.
Usage
submean(y, PSU, N, M, Nl, m.weight, n.weight, method = 'simple', level = 0.95)
Arguments
y |
vector of target variable. |
PSU |
vector of grouping variable which indicates the primary unit for each sample element. |
N |
positive integer specifying population size |
M |
positive integer specifying the number of primary units in the population. |
Nl |
vector of sample sizes in each primary unit, which has to be specified in alphabetical or numerical order of the categories of l. |
m.weight |
vector of primary sample unit weights, which has to be specified in alphabetical or numerical order of the categories of l. |
n.weight |
vector of secondary sample unit weights in each primary sample unit, which has to be specified in alphabetical or numerical order of the categories of l. |
method |
estimation method. Default is "simple", alternative is "ratio". |
level |
coverage probability for confidence intervals. Default is |
Details
If the absolute sizes M
and Nl
are given, the variances are calculated with finite population correction. Otherwise, if the weights m.weight
and n.weight
are given, the variances are calculated without finite population correction.
Value
The function submean
returns a value, which is a list consisting of the components
call |
is a list of call components: |
mean |
mean estimate for population |
se |
standard error of the mean estimate for population |
ci |
vector of confidence interval boundaries for population |
Author(s)
Shuai Shao and Juliane Manitz
References
Kauermann, Goeran/Kuechenhoff, Helmut (2011): Stichproben. Methoden und praktische Umsetzung mit R. Springer.
See Also
Examples
y <- c(23,33,24,25,72,74,71,37,42)
psu <- as.factor(c(1,1,1,1,2,2,2,3,3))
# with finite population correction
submean(y, PSU=psu, N=700, M=23, Nl=c(100,50,75), method='ratio')
# without finite population correction
submean(y, PSU=psu, N=700, m.weight=3/23, n.weight=c(4/100,3/50,2/75), method='ratio')
# Chinese wage data
data(wage)
summary(wage)
submean(wage$Wage,PSU=wage$Region, N=990, M=33, Nl=rep(30,14))