bayesx.term.options {R2BayesX} | R Documentation |
Show BayesX Term Options
Description
BayesX model terms specified using functions sx
may have
additional optional control arguments. Therefore function
bayesx.term.options
displays the possible additional controlling parameters for a
particular model term.
Usage
bayesx.term.options(bs = "ps", method = "MCMC")
Arguments
bs |
character, the term specification for which controlling parameters should be shown. |
method |
character, for which method should additional arguments be shown, options are
|
Details
At the moment the following model terms are implemented, for which additional controlling parameters may be specified:
-
"rw1"
,"rw2"
: Zero degree P-splines: Defines a zero degree P-spline with first or second order difference penalty. A zero degree P-spline typically estimates for every distinct covariate value in the dataset a separate parameter. Usually there is no reason to prefer zero degree P-splines over higher order P-splines. An exception are ordinal covariates or continuous covariates with only a small number of different values. For ordinal covariates higher order P-splines are not meaningful while zero degree P-splines might be an alternative to modeling nonlinear relationships via a dummy approach with completely unrestricted regression parameters. -
"season"
: Seasonal effect of a time scale. -
"ps"
,"psplinerw1"
,"psplinerw2"
: P-spline with first or second order difference penalty. -
"te"
,"pspline2dimrw1"
: Defines a two-dimensional P-spline based on the tensor product of one-dimensional P-splines with a two-dimensional first order random walk penalty for the parameters of the spline. -
"kr"
,"kriging"
: Kriging with stationary Gaussian random fields. -
"gk"
,"geokriging"
: Geokriging with stationary Gaussian random fields: Estimation is based on the centroids of a map object provided in boundary format (see functionread.bnd
andshp2bnd
) as an additional argument namedmap
within functionsx
, or supplied within argumentxt
when using functions
, e.g.,xt = list(map = MapBnd)
. -
"gs"
,"geospline"
: Geosplines based on two-dimensional P-splines with a two-dimensional first order random walk penalty for the parameters of the spline. Estimation is based on the coordinates of the centroids of the regions of a map object provided in boundary format (see functionread.bnd
andshp2bnd
) as an additional argument namedmap
(see above). -
"mrf"
,"spatial"
: Markov random fields: Defines a Markov random field prior for a spatial covariate, where geographical information is provided by a map object in boundary or graph file format (see functionread.bnd
,read.gra
andshp2bnd
), as an additional argument namedmap
(see above). -
"bl"
,"baseline"
: Nonlinear baseline effect in hazard regression or multi-state models: Defines a P-spline with second order random walk penalty for the parameters of the spline for the log-baseline effectlog(\lambda(time))
. -
"factor"
: Special BayesX specifier for factors, especially meaningful ifmethod = "STEP"
, since the factor term is then treated as a full term, which is either included or removed from the model. -
"ridge"
,"lasso"
,"nigmix"
: Shrinkage of fixed effects: defines a shrinkage-prior for the corresponding parameters\gamma_j
,j = 1, \ldots, q
,q \geq 1
of the linear effectsx_1, \ldots, x_q
. There are three priors possible: ridge-, lasso- and Normal Mixture of inverse Gamma prior. -
"re"
: Gaussian i.i.d. Random effects of a unit or cluster identification covariate.
Author(s)
Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Achim Zeileis.
Examples
## show arguments for P-splines
bayesx.term.options(bs = "ps")
bayesx.term.options(bs = "ps", method = "REML")
## Markov random fields
bayesx.term.options(bs = "mrf")