Description Usage Arguments Details Value Demonstration References See Also
Calculates the Laplace approximation to the uni- and bivariate marginal densities of components of the MLE in a regression-scale model. The reference distribution is the conditional distribution given the ancillary.
1 2 |
which |
the kind of marginal density that should be approximated.
Possible choices are |
data |
a special conditional sampling data object. This object must be a list with the following elements:
The |
val1 |
sequence of values for the first MLE at which to calculate the density. |
idx1 |
index of the first regression coefficient, that is, its position in the vector MLE. |
val2 |
sequence of values for the second MLE at which to calculate the density. |
idx2 |
index of the second regression coefficient, that is, its position in the vector MLE. |
log.scale |
logical value. If |
Laplace's integral approximation method is used in order to avoid
multi-dimensional numerical integration. The uni- and bivariate
approximations to the marginal distributions give insight into how
the multivariate conditional distribution of the MLE
vector is structured. Methods are available to plot them. They
help in choosing a suitable candidate generation density to be used
in the rsm.sample
function.
All information is supplied through the data
argument. Note
that the user has to keep to the structure described above. If a
conditional simulation is to be performed for a fitted rsm
object, the make.sample.data
function can be
used to generate this special object. The logical switch
fixed
in the conditional sampling data object must be
specified.
Returns a Lapl.spl
or Lapl.cont
object with the
approximate uni- or bivariate conditional distribution of one or two
components of the MLE.
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.
make.sample.data
,
rsm.sample
.
family.rsm.object
,
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