| sreg_poisson {sregsurvey} | R Documentation |
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 <cardozorpackages@gmail.com>
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