Uses a fitted rsm model to create the data object used by
the conditional sampler rsm.sample.
make.sample.data(rsmObject)a fitted rsm object.
Returns a conditional sampling data object such as needed by
the rsm.sample function. This object is a list with the
following elements:
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.
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.
the vector of true values of the regression coefficients, that is, the values used in the simulation study.
the true value of the scale parameter used in the simulation study.
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.
a logical value. If TRUE the scale parameter is known.
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).
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.