`BayesX.Rd`

This sampler function for BAMLSS is an interface to the BayesX
(`https://www.uni-goettingen.de/de/bayesx/550513.html`

)
command-line binary from R. The sampler is based on the command line version and functions
provided in the BayesXsrc package, which can be installed using
function `get_BayesXsrc()`

.

```
## Sampler functions:
sam_BayesX(x, y, family, start = NULL, weights = NULL, offset = NULL,
data = NULL, control = BayesX.control(...), ...)
BayesX(x, y, family, start = NULL, weights = NULL, offset = NULL,
data = NULL, control = BayesX.control(...), ...)
## Sampler control:
BayesX.control(n.iter = 1200, thin = 1, burnin = 200,
seed = NULL, predict = "light", model.name = "bamlss",
data.name = "d", prg.name = NULL, dir = NULL,
verbose = FALSE, show.prg = TRUE, modeonly = FALSE, ...)
## Special BayesX smooth term constructor.
sx(x, z = NULL, bs = "ps", by = NA, ...)
## Special BayesX tensor product smooth term constructor.
tx(..., bs = "ps", k = -1,
ctr = c("center", "main", "both", "both1", "both2",
"none", "meanf", "meanfd", "meansimple", "nullspace"),
xt = NULL, special = TRUE)
tx2(...)
tx3(..., bs = "ps", k = c(10, 5),
ctr = c("main", "center"),
xt = NULL, special = TRUE)
tx4(..., ctr = c("center", "main", "both", "both1", "both2"))
## Smooth constructors and predict matrix.
# S3 method for tensorX.smooth.spec
smooth.construct(object, data, knots, ...)
# S3 method for tensorX.smooth
Predict.matrix(object, data)
# S3 method for tensorX3.smooth.spec
smooth.construct(object, data, knots, ...)
# S3 method for tensorX3.smooth
Predict.matrix(object, data)
## Family object for quantile regression with BayesX.
quant_bamlss(prob = 0.5)
## Download the newest version of BayesXsrc.
get_BayesXsrc(dir = NULL, install = TRUE)
```

- x
For function

`BayesX()`

the`x`

list, as returned from function`bamlss.frame`

, holding all model matrices and other information that is used for fitting the model. For function`sx()`

arguments`x`

and`z`

specify the variables the smooth should be a function of.- y
The model response, as returned from function

`bamlss.frame`

.- z
Second variable in a

`sx()`

term.- family
A bamlss family object, see

`family.bamlss`

.- start
A named numeric vector containing possible starting values, the names are based on function

`parameters`

.- weights
Prior weights on the data, as returned from function

`bamlss.frame`

.- offset
Can be used to supply model offsets for use in fitting, returned from function

`bamlss.frame`

.- data
The model frame that should be used for modeling. Note that argument

`data`

needs not to be specified when the`BayesX()`

sampler function is used with`bamlss`

. For the smooth constructor for`tx()`

terms, see function`smooth.construct`

.- control
List of control arguments to be send to BayesX. See below.

- n.iter
Sets the number of MCMC iterations.

- thin
Defines the thinning parameter for MCMC simulation. E.g.,

`thin = 10`

means, that only every 10th sampled parameter will be stored.- burnin
Sets the burn-in phase of the sampler, i.e., the number of starting samples that should be removed.

- seed
Sets the seed.

- predict
Not supported at the moment, do not modify!

- model.name
The name that should be used for the model when calling BayesX.

- data.name
The name that should be used for the data set when calling BayesX.

- prg.name
The name that should be used for the

`.prg`

file that is send to BayesX.- dir
Specifies the directory where BayesX should store all output files. For function

`get_BayesXsrc()`

, the directory where BayesXsrc should be stored.- verbose
Print information during runtime of the algorithm.

- show.prg
Show the BayesX

`.prg`

file.- modeonly
Should only the posterior mode be compute, note that this is done using fixed smoothing parameters/variances.

- bs
A

`character`

string, specifying the basis/type which is used for this model term.- by
A by variable for varying coefficient model terms.

- k
The dimension(s) of the bases used to represent the

`tx()`

smooth term.- ...
Not used in

`BayesX.control`

. For function`sx()`

any extra arguments that should be passed to BayesX for this model term can be specified here. For function`tx()`

, all variables the smooth should be a function of are specified here. For function`sam_BayesX()`

all arguments that should be passed to`BayesX.control`

.- ctr
Specifies the type of constraints that should be applied.

`"main"`

, both main effects should be removed;`"both"`

, both main effects and varying effects should be removed;`"none"`

, no constraint should be applied.- xt
A list of extra arguments to be passed to BayesX.

- special
Should the

`tx()`

model term be treated as a special smooth. This must be set to`TRUE`

if using the`sam_BayesX`

sampler and should be set to`FALSE`

, e.g., when using the`sam_GMCMC`

sampler.- object, knots
See, function

`smooth.construct`

.- prob
Numeric, specifies the quantile to be modeled, see the examples.

- install
Should package BayesXsrc be installed?

Function `sam_BayesX()`

writes a BayesX `.prg`

file and processes the data.
Then, the function call the BayesX binary via function
`run.bayesx()`

of the BayesXsrc package. After the BayesX sampler has
finished, the function reads back in all the parameter samples that can then be used
for further processing within `bamlss`

, i.a.

The smooth term constructor functions `s`

and `te`

can
be used with the `sam_BayesX()`

sampler. When using `te`

note that only
one smoothing variance is estimated by BayesX.

For anisotropic penalties use function `tx()`

and `tx3()`

, the former currently
supports smooth functions of two variables, while `tx3()`

is supposed to model space-time
interactions. Note that in `tx3()`

the first variable represents time and the 2nd and 3rd
variable the coordinates in space.

Function `sam_BayesX()`

returns samples of parameters. The samples are provided as a

`mcmc`

matrix.

Function `BayesX.control()`

returns a `list`

with control arguments for

BayesX.

Function `sx()`

a `list`

of class `"xx.smooth.spec"`

and `"no.mgcv"`

, where

`"xx"`

is a basis/type identifying code given by the `bs`

argument.

Function `tx()`

and `tx2()`

a `list`

of class `tensorX.smooth.spec`

.

Note that this interface is still experimental and needs the newest version of the BayesX
source code, which is not yet part of the BayesXsrc package on CRAN. The newest version
can be installed with function `get_BayesXsrc`

. Note that the function assumes that sh,
subversion (svn) and R can be run from the command line!

Note that for setting up a new family object to be used with `sam_BayesX()`

additional
information needs to be supplied. The extra information must be placed within the
family object in an named `list`

element named `"bayesx"`

. For each parameter of
the distribution a character string with the corresponding BayesX `family`

name and the
`equationtype`

must be supplied. See, e.g., the R code of `gaussian_bamlss`

how the setup works.

For function `sx()`

the following basis types are currently supported:

`"ps"`

: P-spline with second order difference penalty.`"mrf"`

: 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`

.`"re"`

: Gaussian i.i.d. Random effects of a unit or cluster identification covariate.

Function `tx()`

currently supports smooth terms with two variables.

```
## Get newest version of BayesXsrc.
## Note: needs sh, svn and R build tools!
## get_BayesXsrc()
if (FALSE) if(require("BayesXsrc")) {
## Simulate some data
set.seed(123)
d <- GAMart()
## Estimate model with BayesX. Note
## that BayesX computes starting values, so
## these are not required by some optimizer function
## in bamlss()
b1 <- bamlss(num ~ s(x1) + s(x2) + s(x3) + s(lon,lat),
data = d, optimizer = FALSE, sampler = sam_BayesX)
plot(b1)
## Same model with anisotropic penalty.
b2 <- bamlss(num ~ s(x1) + s(x2) + s(x3) + tx(lon,lat),
data = d, optimizer = FALSE, sampler = sam_BayesX)
plot(b2)
## Quantile regression.
b3_0.1 <- bamlss(num ~ s(x1) + s(x2) + s(x3) + tx(lon,lat),
data = d, optimizer = FALSE, sampler = sam_BayesX,
family = gF("quant", prob = 0.1))
b3_0.9 <- bamlss(num ~ s(x1) + s(x2) + s(x3) + tx(lon,lat),
data = d, optimizer = FALSE, sampler = sam_BayesX,
family = gF("quant", prob = 0.9))
## Predict quantiles.
p_0.1 <- predict(b3_0.1, term = "s(x2)")
p_0.9 <- predict(b3_0.9, term = "s(x2)")
## Plot.
plot2d(p_0.1 + p_0.9 ~ x2, data = d)
}
```