Estimates prediction error for a function-on-scalar regression model by leave-one-function-out cross-validation (CV), at each of a specified set of points.
pwcv(
fdobj,
Z,
L = NULL,
lambda,
eval.pts = seq(min(fdobj$basis$range), max(fdobj$basis$range), length.out = 201),
scale = FALSE
)
A vector of the same length as eval.pts
giving the CV
scores.
a functional data object (class fd
) giving the
functional responses.
the model matrix, whose columns represent scalar predictors.
a row vector or matrix of linear contrasts of the coefficient functions, to be restricted to equal zero.
smoothing parameter: either a nonnegative scalar or a vector,
of length ncol(Z)
, of nonnegative values.
argument values at which the CV score is to be evaluated.
logical value or vector determining scaling of the matrix
Z
(see scale
, to which the value of this argument is
passed).
Philip Reiss phil.reiss@nyumc.org
Integrating the pointwise CV estimate over the function domain yields the
cross-validated integrated squared error, the standard overall model
fit score returned by lofocv
.
It may be desirable to derive the value of lambda
from an
appropriate call to fosr
, as in the example below.
Reiss, P. T., Huang, L., and Mennes, M. (2010). Fast function-on-scalar regression with penalized basis expansions. International Journal of Biostatistics, 6(1), article 28. Available at https://pubmed.ncbi.nlm.nih.gov/21969982/
fosr
, lofocv