TwoStageControl-class: Class "TwoStageControl"
In simFrame: Simulation Framework

Description Usage Arguments Objects from the Class Slots Details Extends Accessor and mutator methods Methods UML class diagram Author(s) See Also Examples

Class for controlling the setup of samples using a two-stage procedure.

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TwoStageControl(..., fun1 = srs, fun2 = srs, size1 = NULL, 
                size2 = NULL, prob1 = NULL, prob2 = NULL, 
                dots1 = list(), dots2 = list())

`...`	the slots for the new object (see below).
`fun1`	the function to be used for sampling in the first stage (the first list component of slot `fun`).
`fun2`	the function to be used for sampling in the second stage (the second list component of slot `fun`).
`size1`	the number of PSUs to sample in the first stage (the first list component of slot `size`).
`size2`	the number of items to sample in the second stage (the second list component of slot `size`).
`prob1`	the probability weights for the first stage (the first list component of slot `prob`).
`prob2`	the probability weights for the second stage (the second list component of slot `prob`).
`dots1`	additional arguments to be passed to the function for sampling in the first stage (the first list component of slot `dots`).
`dots2`	additional arguments to be passed to the function for sampling in the second stage (the second list component of slot `dots`).

Objects can be created by calls of the form new("TwoStageControl", ...) or via the constructor TwoStageControl.

design:: Object of class "BasicVector" specifying variables (columns) to be used for stratified sampling in the first stage.
grouping:: Object of class "BasicVector" specifying grouping variables (columns) to be used for sampling primary sampling units (PSUs) and secondary sampling units (SSUs), respectively.
fun:: Object of class "list"; a list of length two containing the functions to be used for sampling in the first and second stage, respectively (defaults to srs for both stages). The functions should return a vector containing the indices of the sampled items.
size:: Object of class "list"; a list of length two, where each component contains an optional non-negative integer giving the number of items to sample in the first and second stage, respectively. In case of stratified sampling in the first stage, a vector of non-negative integers, each giving the number of PSUs to sample from the corresponding stratum, may be supplied. For the second stage, a vector of non-negative integers giving the number of items to sample from each PSU may be used.
prob:: Object of class "list"; a list of length two, where each component gives optional probability weights for the first and second stage, respectively. Each component may thereby be a numerical vector, or a character string or integer vector specifying a variable (column) that contains the probability weights.
dots:: Object of class "list"; a list of length two, where each component is again a list containing additional arguments to be passed to the corresponding function for sampling in fun.
k:: Object of class "numeric"; a single positive integer giving the number of samples to be set up.

There are some restrictions on the argument names of the functions for sampling in fun. If the sampling method needs population data as input, the corresponding argument should be called x and should expect a data.frame. If it only needs the population size as input, the argument should be called N. Note that the function is not expected to have both x and N as arguments, and that the latter is typically much faster. Furthermore, if the function has arguments for sample size and probability weights, they should be called size and prob, respectively. Note that a function with prob as its only argument is perfectly valid (for probability proportional to size sampling). Further arguments may be supplied as a list via the slot dots.

Class "VirtualSampleControl", directly. Class "OptSampleControl", by class "VirtualSampleControl", distance 2.

In addition to the accessor and mutator methods for the slots inherited from "VirtualSampleControl", the following are available:

getDesign: signature(x = "TwoStageControl"): get slot design.
setDesign: signature(x = "TwoStageControl"): set slot design.
getGrouping: signature(x = "TwoStageControl"): get slot grouping.
setGrouping: signature(x = "TwoStageControl"): set slot grouping.
getCollect: signature(x = "TwoStageControl"): get slot collect.
setCollect: signature(x = "TwoStageControl"): set slot collect.
getFun: signature(x = "TwoStageControl"): get slot fun.
setFun: signature(x = "TwoStageControl"): set slot fun.
getSize: signature(x = "TwoStageControl"): get slot size.
setSize: signature(x = "TwoStageControl"): set slot size.
getProb: signature(x = "TwoStageControl"): get slot prob.
setProb: signature(x = "TwoStageControl"): set slot prob.
getDots: signature(x = "TwoStageControl"): get slot dots.
setDots: signature(x = "TwoStageControl"): set slot dots.

In addition to the methods inherited from "VirtualSampleControl", the following are available:

clusterSetup: signature(cl = "ANY", x = "data.frame", control = "TwoStageControl"): set up multiple samples on a cluster.
setup: signature(x = "data.frame", control = "TwoStageControl"): set up multiple samples.
show: signature(object = "TwoStageControl"): print the object on the R console.

A slightly simplified UML class diagram of the framework can be found in Figure 1 of the package vignette An Object-Oriented Framework for Statistical Simulation: The R Package simFrame. Use vignette("simFrame-intro") to view this vignette.

Andreas Alfons

"VirtualSampleControl", "SampleControl", "SampleSetup", setup, draw