In most cases essentially a wrapper for predict.gam for prediction from a
model fitted by bam. Can compute on a parallel cluster. For models fitted using discrete
methods with discrete=TRUE then discrete prediction methods are used instead.
Takes a fitted bam object produced by bam
and produces predictions given a new set of values for the model covariates
or the original values used for the model fit. Predictions can be accompanied
by standard errors, based on the posterior distribution of the model
coefficients. The routine can optionally return the matrix by which the model
coefficients must be pre-multiplied in order to yield the values of the linear predictor at
the supplied covariate values: this is useful for obtaining credible regions
for quantities derived from the model (e.g. derivatives of smooths), and for lookup table prediction outside
R.
# S3 method for bam
predict(object,newdata,type="link",se.fit=FALSE,terms=NULL,
exclude=NULL,block.size=50000,newdata.guaranteed=FALSE,
na.action=na.pass,cluster=NULL,discrete=TRUE,n.threads=1,gc.level=0,...)If type=="lpmatrix" then a matrix is returned which will
give a vector of linear predictor values (minus any offest) at the supplied covariate
values, when applied to the model coefficient vector.
Otherwise, if se.fit is TRUE then a 2 item list is returned with items (both arrays) fit
and se.fit containing predictions and associated standard error estimates, otherwise an
array of predictions is returned. The dimensions of the returned arrays depends on whether
type is "terms" or not: if it is then the array is 2 dimensional with each
term in the linear predictor separate, otherwise the array is 1 dimensional and contains the
linear predictor/predicted values (or corresponding s.e.s). The linear predictor returned termwise will
not include the offset or the intercept.
newdata can be a data frame, list or model.frame: if it's a model frame
then all variables must be supplied.
a fitted bam object as produced by bam.
A data frame or list containing the values of the model covariates at which predictions
are required. If this is not provided then predictions corresponding to the
original data are returned. If newdata is provided then
it should contain all the variables needed for prediction: a
warning is generated if not.
When this has the value "link" (default) the linear predictor (possibly with
associated standard errors) is returned. When type="terms" each component of the
linear predictor is returned seperately (possibly with standard errors): this includes
parametric model components, followed by each smooth component, but excludes
any offset and any intercept. type="iterms" is the same, except that any standard errors
returned for smooth components will include the uncertainty about the intercept/overall mean. When
type="response" predictions
on the scale of the response are returned (possibly with approximate
standard errors). When type="lpmatrix" then a matrix is returned
which yields the values of the linear predictor (minus any offset) when
postmultiplied by the
parameter vector (in this case se.fit is ignored). The latter
option is most useful for getting variance estimates for quantities derived from
the model: for example integrated quantities, or derivatives of smooths. A
linear predictor matrix can also be used to implement approximate prediction
outside R (see example code, below).
when this is TRUE (not default) standard error estimates are returned for each prediction.
if type=="terms" or type="iterms" then only results for the terms (smooth or parametric) named in this array will be returned. Otherwise any terms not named in this array will be set to zero. If NULL then all terms are included. "(Intercept)" is the intercept term.
if type=="terms" or type="iterms" then terms (smooth or parametric) named in this array
will not be returned. Otherwise any terms named in this array will be set to zero.
If NULL then no terms are excluded. To avoid supplying covariate values for excluded smooth terms, set newdata.guaranteed=TRUE,
but note that this skips all checks of newdata.
maximum number of predictions to process per call to underlying code: larger is quicker, but more memory intensive.
Set to TRUE to turn off all checking of
newdata except for sanity of factor levels: this can speed things up
for large prediction tasks, but newdata must be complete, with no
NA values for predictors required in the model.
what to do about NA values in newdata. With the
default na.pass, any row of newdata containing NA values
for required predictors, gives rise to NA predictions (even if the term concerned has no
NA predictors). na.exclude or na.omit result in the
dropping of newdata rows, if they contain any NA values for
required predictors. If newdata is missing then NA handling is
determined from object$na.action.
predict.bam can compute in parallel using parLapply
from the parallel package, if it is supplied with a cluster on which to do this (a cluster here can be some cores of a
single machine). See details and example code for bam.
if TRUE then discrete prediction methods used with model fitted by discrete methods. FALSE for regular prediction. See details.
if se.fit=TRUE and discrete prediction is used then parallel computation can be used to speed up se calcualtion. This specifies number of htreads to use.
increase from 0 to up the level of garbage collection if default does not give enough.
other arguments.
Simon N. Wood simon.wood@r-project.org
The design is inspired by the S function of the same name described in Chambers and Hastie (1993) (but is not a clone).
Predictions are likely to be incorrect if data dependent transformations of the covariates
are used within calls to smooths. See examples in predict.gam.
The standard errors produced by predict.gam are based on the
Bayesian posterior covariance matrix of the parameters Vp in the fitted
bam object.
To facilitate plotting with termplot, if object possesses
an attribute "para.only" and type=="terms" then only parametric
terms of order 1 are returned (i.e. those that termplot can handle).
Note that, in common with other prediction functions, any offset supplied to
bam as an argument is always ignored when predicting, unlike
offsets specified in the bam model formula.
See the examples in predict.gam for how to use the lpmatrix for obtaining credible
regions for quantities derived from the model.
When discrete=TRUE the prediction data in newdata is discretized in the same way as is done when using discrete fitting methods with bam. However the discretization grids are not currently identical to those used during fitting. Instead, discretization is done afresh for the prediction data. This means that if you are predicting for a relatively small set of prediction data, or on a regular grid, then the results may in fact be identical to those obtained without discretization. The disadvantage to this approach is that if you make predictions with a large data frame, and then split it into smaller data frames to make the predictions again, the results may differ slightly, because of slightly different discretization errors.
Chambers and Hastie (1993) Statistical Models in S. Chapman & Hall.
Marra, G and S.N. Wood (2012) Coverage Properties of Confidence Intervals for Generalized Additive Model Components. Scandinavian Journal of Statistics.
Wood S.N. (2006b) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.
bam, predict.gam
## for parallel computing see examples for ?bam
## for general useage follow examples in ?predict.gam
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