predict.bamlss.RdTakes 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, ...)An object of class "bamlss"
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.
Character or integer, specifies the model for which predictions should be computed.
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.
Should partial string matching be used to select the terms for
prediction. Note that, e.g., term = "x1" will select all terms including "x1" if
match.names = TRUE.
Should the intercept be included?
If type = "link" the predictor of the corresponding model
is returned. If type = "parameter" predictions on the distributional parameter scale
are returned.
A function that should be applied on the samples of predictors or
parameters, depending on argument type.
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.
Predictions can be computed from samples or estimated parameters of optimizer functions. If no samples are available the default is to use estimated parameters.
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.
If predictions are chunked, information on the prediction process can be printed.
If predictions for only one model are returned, the list structure is dropped.
Specifies the number of cores that should be used for prediction. Note that
this functionality is based on the parallel package.
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 eval(expr, envir, enclos): object 'd' not found
## Predictions.
p <- predict(b)
#> Error in eval(expr, envir, enclos): object 'b' not found
str(b)
#> Error in eval(expr, envir, enclos): object 'b' not found
## Prediction for "mu" model and term "s(x2)".
p <- predict(b, model = "mu", term = "s(x2)")
#> Error in eval(expr, envir, enclos): object 'b' not found
## Plot effect
plot2d(p ~ x2, data = d)
#> Error in eval(expr, envir, enclos): object 'd' not found
## Same for "sigma" model.
p <- predict(b, model = "sigma", term = "s(x2)")
#> Error in eval(expr, envir, enclos): object 'b' not found
plot2d(p ~ x2, data = d)
#> Error in eval(expr, envir, enclos): 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 eval(expr, envir, enclos): object 'b' not found
## Prediction based on quantiles.
p <- predict(b, model = "mu", term = "s(x2)", FUN = c95)
#> Error in eval(expr, envir, enclos): object 'b' not found
plot2d(p ~ x2, data = d)
#> Error in eval(expr, envir, enclos): 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 eval(expr, envir, enclos): object 'b' not found
print(dim(p))
#> Error in eval(expr, envir, enclos): object 'p' not found
plot2d(p ~ x2, data = d, col.lines = rgb(0.1, 0.1, 0.1, alpha = 0.1))
#> Error in eval(expr, envir, enclos): 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 eval(expr, envir, enclos): object 'b' not found
head(p)
#> Error in eval(expr, envir, enclos): 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 eval(expr, envir, enclos): 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'