predict.pfr {refund} | R Documentation |
Prediction from a fitted pfr model
Description
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
()
Usage
## S3 method for class 'pfr'
predict(
object,
newdata,
type = "response",
se.fit = FALSE,
terms = NULL,
PredOutOfRange = FALSE,
...
)
Arguments
object |
a fitted |
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 |
type |
character; see |
se.fit |
logical; see |
terms |
character see |
PredOutOfRange |
logical; if this argument is true then any functional predictor values in
newdata corresponding to |
... |
additional arguments passed on to |
Value
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
Author(s)
Mathew W. McLean mathew.w.mclean@gmail.com and Fabian Scheipl
See Also
Examples
######### 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)