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This package provides functions to approximate joint-inclusion probabilities in Unequal Probability Sampling, or to find Monte Carlo approximations of first and second-order inclusion probabilities of a general sampling design.
The main functions are:
jip_approx()
: returns a matrix of approximated joint-inclusion probabilities for
unequal probability sampling design with high entropy;jip_MonteCarlo()
: produces a matrix of first and second order inclusion probabilities
for a given sampling design, approximated through Monte Carlo simulation.
This method of approximation is more flexible but also computer-intensive.HTvar()
: returns the Horvitz-Thompson or Sen-Yates-Grundy variance or their estimates,
computed using true inclusion probabilities or an approximation obtained by
jip_approx()
or jip_MonteCarlo()
.The development version of the package can be installed from GitHub:
# if not present, install 'devtools' package
install.packages("devtools")
devtools::install_github("rhobis/jipApprox")
library(jipApprox)
### Generate population data ---
N <- 20; n <- 5
set.seed(0)
x <- rgamma(500, scale=10, shape=5)
y <- abs( 2*x + 3.7*sqrt(x) * rnorm(N) )
pik <- n * x/sum(x)
### Approximate joint-inclusion probabilities for high entropy designs ---
pikl <- jip_approx(pik, method='Hajek')
pikl <- jip_approx(pik, method='HartleyRao')
pikl <- jip_approx(pik, method='Tille')
pikl <- jip_approx(pik, method='Brewer1')
pikl <- jip_approx(pik, method='Brewer2')
pikl <- jip_approx(pik, method='Brewer3')
pikl <- jip_approx(pik, method='Brewer4')
### Approximate inclusion probabilities through Monte Carlo simulation ---
pikl <- jip_MonteCarlo(x=pik, n = n, replications = 100, design = "brewer")
pikl <- jip_MonteCarlo(x=pik, n = n, replications = 100, design = "tille")
pikl <- jip_MonteCarlo(x=pik, n = n, replications = 100, design = "poisson")
pikl <- jip_MonteCarlo(x=pik, n = n, replications = 100, design = "maxEntropy")
pikl <- jip_MonteCarlo(x=pik, n = n, replications = 100, design = "randomSystematic")
pikl <- jip_MonteCarlo(x=pik, n = n, replications = 100, design = "systematic")
pikl <- jip_MonteCarlo(x=pik, n = n, replications = 100, design = "sampford")
rob.sichera@gmail.com
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