pinc: Inclusion probabilities in a PIps design
In optimStrat: Choosing the Sample Strategy
pinc R Documentation
Inclusion probabilities in a PIps design
Description
Compute the inclusion probabilities to be used in a PIps design with sample size equal to n.
Usage
pinc(n, x)
Arguments
n
a positive integer indicating the desired sample size.
x
a positive numeric vector giving the values of the auxiliary variable.
Details
The inclusion probabilities are calculated as n\times x_{k}/t_{x} and corrected, if necessary, to ensure that they are smaller or equal than one.
Value
A numeric vector giving the inclusion probability of each element.
Examples
x<- 1 + sort( rgamma(100, shape=4/9, scale=108) )
pinc(n=30,x)
optimStrat documentation built on Aug. 24, 2023, 9:09 a.m.
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| pinc | R Documentation |
Inclusion probabilities in a PIps design
Description
Compute the inclusion probabilities to be used in a PIps design with sample size equal to n.
Usage
pinc(n, x)
Arguments
n |
a positive integer indicating the desired sample size. |
x |
a positive numeric vector giving the values of the auxiliary variable. |
Details
The inclusion probabilities are calculated as n\times x_{k}/t_{x} and corrected, if necessary, to ensure that they are smaller or equal than one.
Value
A numeric vector giving the inclusion probability of each element.
Examples
x<- 1 + sort( rgamma(100, shape=4/9, scale=108) )
pinc(n=30,x)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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