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.
bayesx.term.options(bs = "ps", method = "MCMC")
character, the term specification for which controlling parameters should be shown.
character, for which method should additional arguments be shown, options are
"MCMC", "REML" and "STEP".
Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Achim Zeileis.
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 function read.bnd and shp2bnd) as an additional
argument named map within function sx, or supplied within argument
xt when using function s, 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 function read.bnd and
shp2bnd) as an additional argument named map (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 function read.bnd, read.gra and
shp2bnd), as an additional argument named map (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 effect \(log(\lambda(time))\).
"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.
"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 effects \(x_1, \ldots, x_q\). 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.
## 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")
Run the code above in your browser using DataLab