submean.Rd
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.
submean(y, PSU, N, M, Nl, m.weight, n.weight, method = 'simple', level = 0.95)
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 |
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.
The function submean
returns a value, which is a list consisting of the components
is a list of call components: y
target variable in sample data, PSU
gouping variable in sample data, N
population size, M
number of primary population units, fpc
finite population correction, method
estimation method, level
coverage probability for confidence intervals
mean estimate for population
standard error of the mean estimate for population
vector of confidence interval boundaries for population
Kauermann, Goeran/Kuechenhoff, Helmut (2011): Stichproben. Methoden und praktische Umsetzung mit R. Springer.
Shuai Shao and Juliane Manitz
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')#> #> submean object: Sub-sample mean estimate #> With finite population correction. #> Using method: ratio #> #> Mean estimate: 40.9074 #> Standard error: 26.9358 #> 95% confidence interval: [-11.8858,93.7006] #># 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')#> #> submean object: Sub-sample mean estimate #> Without finite population correction. #> Using method: ratio #> #> Mean estimate: 40.9074 #> Standard error: 28.8828 #> 95% confidence interval: [-15.7018,97.5166] #>#> Region Sector Wage #> Henan : 19 BusinessServices: 11 Min. : 4698 #> Liaoning : 19 Husbandry : 11 1st Qu.:13918 #> Chongqing: 18 Construction : 10 Median :16695 #> Beijing : 17 Financial : 10 Mean :16760 #> Hubei : 17 Health : 10 3rd Qu.:19533 #> Jiangsu : 17 Research : 10 Max. :29530 #> (Other) :124 (Other) :169#> #> submean object: Sub-sample mean estimate #> With finite population correction. #> Using method: simple #> #> Mean estimate: 16753.19 #> Standard error: 475.3202 #> 95% confidence interval: [15821.58,17684.8] #>