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(UPSvarApprox)
### 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")
```

- Please, report any bug or issue here.
- For more information, please contact the maintainer at
`[email protected]`

.

rhobis/jipApprox documentation built on Jan. 30, 2019, 9:24 p.m.

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