This function fits a functional single-index model (FSIM) between a functional covariate and a scalar response. It employs kernel estimation with Nadaraya-Watson weights and uses B-spline expansions to represent curves and eligible functional indexes.
The function also utilises the leave-one-out cross-validation (LOOCV) criterion to select the bandwidth (h.opt) and the coefficients of the functional index in the spline basis (theta.est). It performs a joint minimisation of the LOOCV objective function in both the bandwidth and the functional index.
fsim.kernel.fit(x, y, seed.coeff = c(-1, 0, 1), order.Bspline = 3,
nknot.theta = 3, min.q.h = 0.05, max.q.h = 0.5, h.seq = NULL, num.h = 10,
kind.of.kernel = "quad", range.grid = NULL, nknot = NULL, n.core = NULL)The matched call.
Estimated scalar response.
Differences between y and the fitted.values.
Coefficients of \(\hat{\theta}\) in the B-spline basis: a vector of length(order.Bspline+nknot.theta).
Selected bandwidth.
Coefficient of determination.
Redidual variance.
Residual degrees of freedom.
Predicted values for the scalar response using leave-one-out samples.
Minimum value of the CV function, i.e. the value of CV for theta.est and h.opt.
Vector containing CV values for each functional index in \(\Theta_n\) and the value of \(h\) that minimises the CV for such index (i.e. CV.values[j] contains the value of the CV function corresponding to theta.seq.norm[j,] and the best value of the h.seq for this functional index according to the CV criterion).
Hat matrix.
Index of \(\hat{\theta}\) in the set \(\Theta_n\).
The vector theta.seq.norm[j,] contains the coefficientes in the B-spline basis of the jth functional index in \(\Theta_n\).
Sequence of eligible values for \(h\).
Matrix containing the observations of the functional covariate (i.e. curves) collected by row.
Vector containing the scalar response.
Vector of initial values used to build the set \(\Theta_n\) (see section Details). The coefficients for the B-spline representation of each eligible functional index \(\theta \in \Theta_n\) are obtained from seed.coeff. The default is c(-1,0,1).
Positive integer giving the order of the B-spline basis functions. This is the number of coefficients in each piecewise polynomial segment. The default is 3
Positive integer indicating the number of regularly spaced interior knots in the B-spline expansion of \(\theta_0\). The default is 3.
Minimum quantile order of the distances between curves, which are computed using the projection semi-metric. This value determines the lower endpoint of the range from which the bandwidth is selected. The default is 0.05.
Maximum quantile order of the distances between curves, which are computed using the projection semi-metric. This value determines the upper endpoint of the range from which the bandwidth is selected. The default is 0.5.
Vector containing the sequence of bandwidths. The default is a sequence of num.h equispaced bandwidths in the range constructed using min.q.h and max.q.h.
Positive integer indicating the number of bandwidths in the grid. The default is 10.
The type of kernel function used. Currently, only Epanechnikov kernel ("quad") is available.
Vector of length 2 containing the endpoints of the grid at which the observations of the functional covariate x are evaluated (i.e. the range of the discretisation). If range.grid=NULL, then range.grid=c(1,p) is considered, where p is the discretisation size of x (i.e. ncol(x)).
Positive integer indicating the number of interior knots for the B-spline expansion of the functional covariate. The default value is (p - order.Bspline - 1)%/%2.
Number of CPU cores designated for parallel execution.The default is n.core<-availableCores(omit=1).
German Aneiros Perez german.aneiros@udc.es
Silvia Novo Diaz snovo@est-econ.uc3m.es
The functional single-index model (FSIM) is given by the expression: $$Y_i=r(\langle\theta_0,X_i\rangle)+\varepsilon_i, \quad i=1,\dots,n,$$ where \(Y_i\) denotes a scalar response, \(X_i\) is a functional covariate valued in a separable Hilbert space \(\mathcal{H}\) with an inner product \(\langle \cdot, \cdot\rangle\). The term \(\varepsilon\) denotes the random error, \(\theta_0 \in \mathcal{H}\) is the unknown functional index and \(r(\cdot)\) denotes the unknown smooth link function.
The FSIM is fitted using the kernel estimator $$ \widehat{r}_{h,\hat{\theta}}(x)=\sum_{i=1}^nw_{n,h,\hat{\theta}}(x,X_i)Y_i, \quad \forall x\in\mathcal{H}, $$ with Nadaraya-Watson weights $$ w_{n,h,\hat{\theta}}(x,X_i)=\frac{K\left(h^{-1}d_{\hat{\theta}}\left(X_i,x\right)\right)}{\sum_{i=1}^nK\left(h^{-1}d_{\hat{\theta}}\left(X_i,x\right)\right)}, $$ where
the real positive number \(h\) is the bandwidth.
\(K\) is a kernel function (see the argument kind.of.kernel).
\(d_{\hat{\theta}}(x_1,x_2)=|\langle\hat{\theta},x_1-x_2\rangle|\) is the projection semi-metric, and \(\hat{\theta}\) is an estimate of \(\theta_0\).
The procedure requires the estimation of the function-parameter \(\theta_0\). Therefore, we use B-spline expansions to represent curves (dimension nknot+order.Bspline) and eligible functional indexes (dimension nknot.theta+order.Bspline). Then, we build a set \(\Theta_n\) of eligible functional indexes by calibrating (to ensure the identifiability of the model) the set of initial coefficients given in seed.coeff. The larger this set is, the greater the size of \(\Theta_n\). Since our approach requires intensive computation, a trade-off between the size of \(\Theta_n\) and the performance of the estimator is necessary. For that, Ait-Saidi et al. (2008) suggested considering order.Bspline=3 and seed.coeff=c(-1,0,1). For details on the construction of \(\Theta_n\), see Novo et al. (2019).
We obtain the estimated coefficients of \(\theta_0\) in the spline basis (theta.est) and the selected bandwidth (h.opt) by minimising the LOOCV criterion. This function performs a joint minimisation in both parameters, the bandwidth and the functional index, and supports parallel computation. To avoid parallel computation, we can set n.core=1.
Ait-Saidi, A., Ferraty, F., Kassa, R., and Vieu, P. (2008) Cross-validated estimations in the single-functional index model. Statistics, 42(6), 475--494, tools:::Rd_expr_doi("https://doi.org/10.1080/02331880801980377").
Novo S., Aneiros, G., and Vieu, P., (2019) Automatic and location-adaptive estimation in functional single--index regression. Journal of Nonparametric Statistics, 31(2), 364--392, tools:::Rd_expr_doi("https://doi.org/10.1080/10485252.2019.1567726").
See also fsim.kernel.test, predict.fsim.kernel, plot.fsim.kernel.
Alternative procedure fsim.kNN.fit.
# \donttest{
data(Tecator)
y<-Tecator$fat
X<-Tecator$absor.spectra2
#FSIM fit.
ptm<-proc.time()
fit<-fsim.kernel.fit(y[1:160],x=X[1:160,],max.q.h=0.35, nknot=20,
range.grid=c(850,1050),nknot.theta=4)
proc.time()-ptm
fit
names(fit)
# }
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