Takes a fitted `pfr`

-object produced by `pfr`

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. This is a wrapper function for `predict.gam`

()

```
# S3 method for pfr
predict(
object,
newdata,
type = "response",
se.fit = FALSE,
terms = NULL,
PredOutOfRange = FALSE,
...
)
```

object

a fitted `pfr`

object as produced by `pfr`

newdata

a named 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. All variables provided to newdata should be in the format supplied to `pfr`

,
i.e., functional predictors must be supplied as matrices with each row corresponding to one
observed function. Index variables for the functional covariates are reused from the fitted model
object or alternatively can be supplied as attributes of the matrix of functional predictor values.
Any variables in the model not specified in newdata are set to their average values from the data
supplied during fitting the model

type

character; see `predict.gam`

for details

se.fit

logical; see `predict.gam`

for details

terms

character see `predict.gam`

for details

PredOutOfRange

logical; if this argument is true then any functional predictor values in
newdata corresponding to `pfr`

terms that are greater[less] than the maximum[minimum] of the
domain of the marginal basis for the rows of the tensor product smooth are set to the maximum[minimum]
of the domain. If this argument is false, attempting to predict a value of the functional predictor
outside the range of this basis produces an error

...

additional arguments passed on to `predict.gam`

If `type == "lpmatrix"`

, the design matrix for the supplied covariate values in long
format. If `se == TRUE`

, a list with entries fit and se.fit containing fits and standard errors,
respectively. If `type == "terms" or "iterms"`

each of these lists is a list of matrices of the
same dimension as the response for newdata containing the linear predictor and its se for each term

# NOT RUN { ######### Octane data example ######### data(gasoline) N <- length(gasoline$octane) wavelengths = 2*450:850 nir = matrix(NA, 60,401) test <- sample(60,20) for (i in 1:60) nir[i,] = gasoline$NIR[i, ] # changes class from AsIs to matrix y <- gasoline$octane #fit <- pfr(y~af(nir,argvals=wavelengths,k=c(6,6), m=list(c(2,2),c(2,2))), # subset=(1:N)[-test]) #preds <- predict(fit,newdata=list(nir=nir[test,]),type='response') #plot(preds,y[test]) #abline(a=0,b=1) # }