The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.