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.
character, the term specification for which controlling parameters should be shown.
character, for which method should additional arguments be shown, options are
At the moment the following model terms are implemented, for which additional controlling parameters may be specified:
"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
"season": Seasonal effect of a time scale.
"psplinerw2": P-spline with first or second order
"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.
"kriging": Kriging with stationary Gaussian random fields.
"geokriging": Geokriging with stationary Gaussian random fields: Estimation
is based on the centroids of a map object provided in
boundary format (see function
shp2bnd) as an additional
map within function
sx, or supplied within argument
xt when using function
xt = list(map = MapBnd).
"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 function
shp2bnd) as an additional argument named
map (see above).
"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 function
shp2bnd), as an additional argument named
map (see above).
"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 effect log(λ(time)).
"factor": Special BayesX specifier for factors, especially meaningful if
method = "STEP", since the factor term is then treated as a full term,
which is either included or removed from the model.
"nigmix": Shrinkage of fixed effects: defines a
shrinkage-prior for the corresponding parameters
γ_j, j = 1, …, q, q ≥q 1 of the
linear effects x_1, …, 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.
Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Achim Zeileis.
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