`predict.bamlss.Rd`

Takes a fitted `bamlss`

object and computes predictions. Predictions can be based on
estimated parameters of optimizer functions or on samples returned from sampler functions.
It is possible to compute predictions on multiple cores using the
`parallel`

and to chunk predictions to save computation time and memory
storage. Predictions can be computed for full distributional parameters or specific model
terms. If a `link{bamlss}`

model was fitted on multiple cores, i.e., the samples
are provided as `link{mcmc.list}`

where each list entry represents samples from one
core, function `predict.bamlss()`

computes combined predictions based on samples of all
cores.

```
# S3 method for bamlss
predict(object, newdata, model = NULL, term = NULL,
match.names = TRUE, intercept = TRUE, type = c("link", "parameter"),
FUN = function(x) { mean(x, na.rm = TRUE) }, trans = NULL,
what = c("samples", "parameters"), nsamps = NULL,
verbose = FALSE, drop = TRUE,
cores = NULL, chunks = 1, ...)
```

- object
An object of class

`"bamlss"`

- newdata
A data frame or list containing the values of the model covariates at which predictions are required. Note that depending on argument

`term`

, only covariates that are needed by the corresponding model terms need to be supplied.- model
Character or integer, specifies the model for which predictions should be computed.

- term
Character or integer, specifies the model terms for which predictions are required. Note that, e.g.,

`term = c("s(x1)", "x2")`

will compute the combined prediction`s(x1) + x2`

.- match.names
Should partial string matching be used to select the

`term`

s for prediction. Note that, e.g.,`term = "x1"`

will select all terms including`"x1"`

if`match.names = TRUE`

.- intercept
Should the intercept be included?

- type
If

`type = "link"`

the predictor of the corresponding`model`

is returned. If`type = "parameter"`

predictions on the distributional parameter scale are returned.- FUN
A function that should be applied on the samples of predictors or parameters, depending on argument

`type`

.- trans
A transformer function or named list of transformer functions that computes transformed predictions. If

`trans`

is a list, the list names must match the names of the parameters of the`bamlss.family`

.- what
Predictions can be computed from samples or estimated parameters of optimizer functions. If no samples are available the default is to use estimated parameters.

- nsamps
If the fitted

`bamlss`

object contains samples of parameters, computing predictions may take quite some time. Therefore, to get a first feeling it can be useful to compute predictions only based on`nsamps`

samples, i.e.,`nsamps`

specifies the number of samples which are extracted on equidistant intervals.- verbose
If predictions are chunked, information on the prediction process can be printed.

- drop
If predictions for only one

`model`

are returned, the list structure is dropped.- cores
Specifies the number of cores that should be used for prediction. Note that this functionality is based on the

`parallel`

package.- chunks
Should computations be split into

`chunks`

? Prediction is then processed sequentially.- ...
Arguments passed to prediction functions that are part of a

`bamlss.family`

object, i.e., the objects has a`$predict()`

function that should be used instead.

Depending on arguments `model`

, `FUN`

and the structure of the `bamlss`

model, a list of predictions or simple vectors or matrices of predictions.

`link{bamlss}`

, `fitted.bamlss`

.

```
if (FALSE) ## Generate some data.
d <- GAMart()
## Model formula.
f <- list(
num ~ s(x1) + s(x2) + s(x3) + te(lon,lat),
sigma ~ s(x1) + s(x2) + s(x3) + te(lon,lat)
)
## Estimate model.
b <- bamlss(f, data = d)
#> Error in bamlss(f, data = d): object 'd' not found
## Predictions.
p <- predict(b)
#> Error in predict(b): object 'b' not found
str(b)
#> Error in str(b): object 'b' not found
## Prediction for "mu" model and term "s(x2)".
p <- predict(b, model = "mu", term = "s(x2)")
#> Error in predict(b, model = "mu", term = "s(x2)"): object 'b' not found
## Plot effect
plot2d(p ~ x2, data = d)
#> Error in plot2d(p ~ x2, data = d): object 'd' not found
## Same for "sigma" model.
p <- predict(b, model = "sigma", term = "s(x2)")
#> Error in predict(b, model = "sigma", term = "s(x2)"): object 'b' not found
plot2d(p ~ x2, data = d)
#> Error in plot2d(p ~ x2, data = d): object 'd' not found
## Prediction for "mu" model and term "s(x1)" + "s(x2)"
## without intercept.
p <- predict(b, model = "mu", term = c("s(x1)", "s(x2)"),
intercept = FALSE)
#> Error in predict(b, model = "mu", term = c("s(x1)", "s(x2)"), intercept = FALSE): object 'b' not found
## Prediction based on quantiles.
p <- predict(b, model = "mu", term = "s(x2)", FUN = c95)
#> Error in predict(b, model = "mu", term = "s(x2)", FUN = c95): object 'b' not found
plot2d(p ~ x2, data = d)
#> Error in plot2d(p ~ x2, data = d): object 'd' not found
## Extract samples of predictor for "s(x2)".
p <- predict(b, model = "mu", term = "s(x2)",
intercept = FALSE, FUN = function(x) { x })
#> Error in predict(b, model = "mu", term = "s(x2)", intercept = FALSE, FUN = function(x) { x}): object 'b' not found
print(dim(p))
#> Error in print(dim(p)): object 'p' not found
plot2d(p ~ x2, data = d, col.lines = rgb(0.1, 0.1, 0.1, alpha = 0.1))
#> Error in plot2d(p ~ x2, data = d, col.lines = rgb(0.1, 0.1, 0.1, alpha = 0.1)): object 'd' not found
## Or using specific combinations of terms.
p <- predict(b, model = "mu", term = c("s(x2)", "te(lon,lat)"),
intercept = FALSE, FUN = function(x) { x })
#> Error in predict(b, model = "mu", term = c("s(x2)", "te(lon,lat)"), intercept = FALSE, FUN = function(x) { x }): object 'b' not found
head(p)
#> Error in head(p): object 'p' not found
## Prediction using new data.
## Only need x3 data when predicting
## for s(x3).
nd <- data.frame("x3" = seq(0, 1, length = 100))
nd <- cbind(nd, predict(b, newdata = nd, term = "s(x3)"))
#> Error in predict(b, newdata = nd, term = "s(x3)"): object 'b' not found
print(head(nd))
#> x3
#> 1 0.00000000
#> 2 0.01010101
#> 3 0.02020202
#> 4 0.03030303
#> 5 0.04040404
#> 6 0.05050505
plot2d(mu ~ x3, data = nd)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.matrix': object 'mu' not found
plot2d(sigma ~ x3, data = nd)
#> Error in as.vector(x, mode): cannot coerce type 'closure' to vector of type 'any'
```