`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] #>