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

In sregsurvey: Semiparametric Model-Assisted Estimation in Finite Populations

Semiparametric Model-Assisted Estimation under a Poisson Sampling Design

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

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.
...	further parameters accepted by caret and survey functions.

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.

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

Author(s)

Carlos Alberto Cardozo Delgado <[e-mail]>

References

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

Cardozo C.A., Paula G., and Vanegas L. (2022). Generalized log-gamma additive partial linear models with P-spline smoothing. Statistical Papers.

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

Examples

library(sregsurvey)
library(survey)
library(dplyr)
library(gamlss)
data(api)
attach(apipop)
Apipop <- filter(apipop,full!= 'NA')
Apipop <- filter(Apipop, stype == 'H')
Apipop <- Apipop %>% dplyr::select(api00,grad.sch,full)
fit <- sreg_poisson(api00 ~  pb(grad.sch), scale_formula = ~ full - 1, data= Apipop)
fit
# The total population value is
true_total <- sum(Apipop$api00)
# The estimated relative bias in percentage is
round(abs((fit$estimated_total_y_sreg - true_total)/true_total),3)*100

sregsurvey documentation built on April 11, 2023, 6:06 p.m.