# Impact point selection with FASSMR and kNN estimation This function implements the Fast Algorithm for Sparse Semiparametric Multi-functional Regression (FASSMR) with kNN estimation. This algorithm is specifically designed for estimating multi-functional partial linear single-index models, which incorporate multiple scalar variables and a functional covariate as predictors. These scalar variables are derived from the discretisation of a curve and have linear effect while the functional covariate exhibits a single-index effect. FASSMR selects the impact points of the discretised curve and estimates the model. The algorithm employs a penalised least-squares regularisation procedure, integrated with kNN estimation using Nadaraya-Watson weights. It uses B-spline expansions to represent curves and eligible functional indexes. Additionally, it utilises an objective criterion (`criterion`) to determine the initial number of covariates in the reduced model (`w.opt`), the number of neighbours (`k.opt`), and the penalisation parameter (`lambda.opt`). ```r FASSMR.kNN.fit(x, z, y, seed.coeff = c(-1, 0, 1), order.Bspline = 3, nknot.theta = 3, knearest = NULL, min.knn = 2, max.knn = NULL, step = NULL, kind.of.kernel = "quad",range.grid = NULL, nknot = NULL, lambda.min = NULL, lambda.min.h = NULL, lambda.min.l = NULL, factor.pn = 1, nlambda = 100, vn = ncol(z), nfolds = 10, seed = 123, wn = c(10, 15, 20), criterion = "GCV", penalty = "grSCAD", max.iter = 1000, n.core = NULL) ``` ## Arguments - `x`: Matrix containing the observations of the functional covariate collected by row (functional single-index component). - `z`: Matrix containing the observations of the functional covariate that is discretised collected by row (linear component). - `y`: Vector containing the scalar response. - `seed.coeff`: 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)`. - `order.Bspline`: 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. - `nknot.theta`: Positive integer indicating the number of regularly spaced interior knots in the B-spline expansion of $\theta_0$. The default is 3. - `knearest`: Vector of positive integers containing the sequence in which the number of nearest neighbours `k.opt` is selected. If `knearest=NULL`, then `knearest <- seq(from =min.knn, to = max.knn, by = step)`. - `min.knn`: A positive integer that represents the minimum value in the sequence for selecting the number of nearest neighbours `k.opt`. This value should be less than the sample size. The default is 2. - `max.knn`: A positive integer that represents the maximum value in the sequence for selecting number of nearest neighbours `k.opt`. This value should be less than the sample size. The default is `max.knn <- n%/%5`. - `step`: A positive integer used to construct the sequence of k-nearest neighbours as follows: `min.knn, min.knn + step, min.knn + 2*step, min.knn + 3*step,...`. The default value for `step` is `step<-ceiling(n/100)`. - `kind.of.kernel`: The type of kernel function used. Currently, only Epanechnikov kernel (`"quad"`) is available. - `range.grid`: 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))`. - `nknot`: 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`. - `lambda.min`: The smallest value for lambda (i. e., the lower endpoint of the sequence in which `lambda.opt` is selected), as fraction of `lambda.max`. The defaults is `lambda.min.l` if the sample size is larger than `factor.pn` times the number of linear covariates and `lambda.min.h` otherwise. - `lambda.min.h`: The lower endpoint of the sequence in which `lambda.opt` is selected if the sample size is smaller than `factor.pn` times the number of linear covariates. The default is 0.05. - `lambda.min.l`: The lower endpoint of the sequence in which `lambda.opt` is selected if the sample size is larger than `factor.pn` times the number of linear covariates. The default is 0.0001. - `factor.pn`: Positive integer used to set `lambda.min`. The default value is 1. - `nlambda`: Positive integer indicating the number of values in the sequence from which `lambda.opt` is selected. The default is 100. - `vn`: Positive integer or vector of positive integers indicating the number of groups of consecutive variables to be penalised together. The default value is `vn=ncol(z)`, resulting in the individual penalization of each scalar covariate. - `nfolds`: Positive integer indicating the number of cross-validation folds (used when `criterion="k-fold-CV"`). Default is 10. - `seed`: You may set the seed for the random number generator to ensure reproducible results (applicable when `criterion="k-fold-CV"` is used). The default seed value is 123. - `wn`: A vector of positive integers indicating the eligible number of covariates in the reduced model. For more information, refer to the section `Details`. The default is `c(10,15,20)`. - `criterion`: The criterion used to select the tuning and regularisation parameters: `wn.opt`, `k.opt` and `lambda.opt` (also `vn.opt` if needed). Options include `"GCV"`, `"BIC"`, `"AIC"`, or `"k-fold-CV"`. The default setting is `"GCV"`. - `penalty`: The penalty function applied in the penalised least-squares procedure. Currently, only "grLasso" and "grSCAD" are implemented. The default is "grSCAD". - `max.iter`: Maximum number of iterations allowed across the entire path. The default value is 1000. - `n.core`: Number of CPU cores designated for parallel execution. The default is `n.core<-availableCores(omit=1)`. ## Details The multi-functional partial linear single-index model (MFPLSIM) is given by the expression $$ Y_i=\sum_{j=1}^{p_n}\beta_{0j}\zeta_i(t_j)+r\left(\left<\theta_0,X_i\right>\right)+\varepsilon_i,\ \ \ (i=1,\dots,n), $$ where: * $Y_i$ is a real random response and $X_i$ denotes a random element belonging to some separable Hilbert space $\mathcal{H}$ with inner product denoted by $\left\langle\cdot,\cdot\right\rangle$. The second functional predictor $\zeta_i$ is assumed to be a curve defined on some interval $[a,b]$ which is observed at the points $a\leq t_1\<\dots\\right)+\varepsilon_i^{\mathbf{1}}. $$ The program receives the eligible numbers of linear covariates for building the reduced model through the argument `wn`. Then, the penalised least-squares variable selection procedure, with kNN estimation, is applied to the reduced model. This is done using the function `sfplsim.kNN.fit`, which requires the remaining arguments (for details, see the documentation of the function `sfplsim.kNN.fit`). The estimates obtained are the outputs of the FASSMR algorithm. For further details on this algorithm, see Novo et al. (2021). Remark : If the condition $p_n=w_n q_n$ is not met (then $p_n/w_n$ is not an integer number), the function considers variable $q_n=q_{n,k}$ values $k=1,\dots,w_n$. Specifically: $$ q_{n,k}= \left\{\begin{array}{ll}[p_n/w_n]+1 & k\in\{1,\dots,p_n-w_n[p_n/w_n]\},\\{[p_n/w_n]} & k\in\{p_n-w_n[p_n/w_n]+1,\dots,w_n\},\end{array}\right. $$ where $[z]$ denotes the integer part of the real number $z$. The function supports parallel computation. To avoid it, we can set `n.core=1`. ## Returns - **call**: The matched call. - **fitted.values**: Estimated scalar response. - **residuals**: Differences between `y` and the `fitted.values`. - **beta.est**: $\hat{\mathbf{\beta}}$ (i.e. estimate of $\mathbf{\beta}_0$ when the optimal tuning parameters `w.opt`, `lambda.opt`, `k.opt` and `vn.opt` are used). - **beta.red**: Estimate of $\beta_0^{\mathbf{1}}$ in the reduced model when the optimal tuning parameters `w.opt`, `lambda.opt`, `k.opt` and `vn.opt` are used. - **theta.est**: Coefficients of $\hat{\theta}$ in the B-spline basis (i.e. estimate of $\theta_0$ when the optimal tuning parameters `w.opt`, `lambda.opt`, `k.opt` and `vn.opt` are used): a vector of `length(order.Bspline+nknot.theta)`. - **indexes.beta.nonnull**: Indexes of the non-zero $\hat{\beta_{j}}$. - **k.opt**: Selected number of nearest neighbours (when `w.opt` is considered). - **w.opt**: Selected size for $\mathcal{R}_n^{\mathbf{1}}$. - **lambda.opt**: Selected value for the penalisation parameter (when `w.opt` is considered). - **IC**: Value of the criterion function considered to select `w.opt`, `lambda.opt`, `k.opt` and `vn.opt`. - **vn.opt**: Selected value of `vn` (when `w.opt` is considered). - **beta.w**: Estimate of $\beta_0^{\mathbf{1}}$ for each value of the sequence `wn` (i.e. for each number of covariates in the reduced model). - **theta.w**: Estimate of $\theta_0^{\mathbf{1}}$ for each value of the sequence `wn` (i.e. its coefficients in the B-spline basis). - **IC.w**: Value of the criterion function for each value of the sequence `wn`. - **indexes.beta.nonnull.w**: Indexes of the non-zero linear coefficients for each value of the sequence `wn`. - **lambda.w**: Selected value of penalisation parameter for each value of the sequence `wn`. - **k.w**: Selected number of neighbours for each value of the sequence `wn`. - **index01**: Indexes of the covariates (in the entire set of $p_n$) used to build $\mathcal{R}_n^{\mathbf{1}}$ for each value of the sequence `wn`. - **...**: ## References Novo, S., Vieu, P., and Aneiros, G., (2021) Fast and efficient algorithms for sparse semiparametric bi-functional regression. **Australian and New Zealand Journal of Statistics**, 63 , 606--638, tools:::Rd_expr_doi("https://doi.org/10.1111/anzs.12355") . ## Author(s) German Aneiros Perez german.aneiros@udc.es Silvia Novo Diaz snovo@est-econ.uc3m.es ## See Also See also `sfplsim.kNN.fit, predict.FASSMR.kNN`, `plot.FASSMR.kNN` and `IASSMR.kNN.fit`. Alternative method `FASSMR.kernel.fit` ## Examples ```r data(Sugar) y<-Sugar$ash x<-Sugar$wave.290 z<-Sugar$wave.240 #Outliers index.y.25 <- y > 25 index.atip <- index.y.25 (1:268)[index.atip] #Dataset to model x.sug <- x[!index.atip,] z.sug<- z[!index.atip,] y.sug <- y[!index.atip] train<-1:216 ptm=proc.time() fit<- FASSMR.kNN.fit(x=x.sug[train,],z=z.sug[train,], y=y.sug[train], nknot.theta=2, lambda.min.l=0.03, max.knn=20,nknot=20,criterion="BIC", max.iter=5000) proc.time()-ptm fit names(fit) ```

FASSMR.kNN.fit function