R: Create a Conditional Sampling Data Object

make.sample.data {csampling}

R Documentation

Create a Conditional Sampling Data Object

Description

Uses a fitted rsm model to create the data object used by the conditional sampler rsm.sample.

Usage

make.sample.data(rsmObject)

Arguments

rsmObject

a fitted rsm object.

Value

Returns a conditional sampling data object such as needed by the rsm.sample function. This object is a list with the following elements:

`anc`	the vector containing the values of the ancillary; usually the Pearson residuals. It has to be of the same length than the number of observations in the linear regression model.
`X`	the model matrix. It may be obtained applying `model.matrix` to the fitted `rsm` object of interest. The number of observations has to be the same than the dimension of the ancillary, and the number of covariates must correspond to the number of regression coefficients defined in the `coef` component.
`coef`	the vector of true values of the regression coefficients, that is, the values used in the simulation study.
`disp`	the true value of the scale parameter used in the simulation study.
`family`	a `family.rsm` object characterizing the error distribution of the linear regression model. The following generator functions are available in the `marg` package of the R package bundle `hoa`: `student` (Student's t), `extreme` (Gumbel or extreme value), `logistic`, `logWeibull`, `logExponential`, `logRayleigh` and `Huber` (Huber's least favourable). The demonstration file ‘margdemo.R’ that accompanies the `marg` package shows how to create a new generator function.
`fixed`	a logical value. If `TRUE` the scale parameter is known.

The make.sample.data function can be used to create this data object from a fitted rsm model.

Demonstration

The file ‘csamplingdemo.R’ contains code that can be used to run a conditional simulation study similar to the one described in Brazzale (2000, Section 7.3) using the data given in Example 3 of DiCiccio, Field and Fraser (1990).

References

Brazzale, A. R. (2000) Practical Small-Sample Parametric Inference. Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.

DiCiccio, T. J., Field, C. A. and Fraser, D. A. S. (1990) Approximations of marginal tail probabilities and inference for scalar parameters. Biometrika, 77, 77–95.