sreg_poisson: Semiparametric Model-Assisted Estimation under a Poisson...

Description Usage Arguments Value Author(s) References Examples

View source: R/sreg_poisson.R

Description

sreg_poisson is used to estimate the total parameter of a finite population generated from a semi-parametric generalized gamma population under a Poisson sampling design.

Usage

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sreg_poisson(location_formula, scale_formula, data, pis)

Arguments

location_formula

a symbolic description of the systematic component of the location model to be fitted.

scale_formula

a symbolic description of the systematic component of the scale model to be fitted.

data

a data frame, list containing the variables in the model.

pis

numeric vector, first order inclusion probabilities. Default value 0.1 for each element.

Value

sampling_design is the name of the sampling design used in the estimation process.

N is the population size.

n is the random sample size used in the estimation process.

first_order_probabilities vector of the first order probabilities used in the estimation process.

sample is the random sample used in the estimation process.

total_y_sreg is the SREG estimate of the total parameter of the finite population.

Author(s)

Carlos Alberto Cardozo Delgado <cardozorpackages@gmail.com>

References

Cardozo C.A, Alonso C. (2021) Semi-parametric model assisted estimation in finite populations. In preparation.

Sarndal C.E., Swensson B., and Wretman J. (2003). Model Assisted Survey Sampling. Springer.

Examples

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library(survey)
library(dplyr)
data(api)
attach(apipop)
Apipop <- filter(apipop,full!= 'NA')
Apipop <- filter(Apipop, stype == 'H')
Apipop <- select(Apipop,c(api00,grad.sch,full))
sreg_poisson(api00 ~  pb(grad.sch), scale_formula = ~ full - 1, data= Apipop)
sum(Apipop$api00)

sregsurvey documentation built on Sept. 15, 2021, 9:09 a.m.