Estimation, Variable Selection and Prediction for Functional Semiparametric Models
Impact point selection with FASSMR and kernel estimation
Impact point selection with FASSMR and kNN estimation
tools:::Rd_package_title("fsemipar")
Regularised fit of sparse linear regression
Package fsemipar internal functions
Functional single-index model fit using kernel estimation and iterativ...
Functional single-index model fit using kernel estimation and joint LO...
Functional single-index kernel predictor
Functional single-index model fit using kNN estimation and iterative L...
Functional single-index model fit using kNN estimation and joint LOOCV...
Functional single-index kNN predictor
Impact point selection with IASSMR and kernel estimation
Impact point selection with IASSMR and kNN estimation
Graphical representation of regression model outputs
Prediction for FSIM
Prediction for MFPLSIM
Prediction for linear models
Prediction for MFPLM
Predictions for SFPLM
Prediction for SFPLSIM and MFPLSIM (using FASSMR)
Summarise information from FSIM estimation
Summarise information from linear models estimation
Summarise information from MFPLM estimation
Summarise information from MFPLSIM estimation
Summarise information from SFPLM estimation
Summarise information from SFPLSIM estimation
Inner product computation
Impact point selection with PVS
Impact point selection with PVS and kernel estimation
Impact point selection with PVS and kNN estimation
Projection semi-metric computation
SFPLM regularised fit using kernel estimation
SFPLM regularised fit using kNN estimation
SFPLSIM regularised fit using kernel estimation
SFPLSIM regularised fit using kNN estimation
Routines for the estimation or simultaneous estimation and variable selection in several functional semiparametric models with scalar responses are provided. These models include the functional single-index model, the semi-functional partial linear model, and the semi-functional partial linear single-index model. Additionally, the package offers algorithms for handling scalar covariates with linear effects that originate from the discretization of a curve. This functionality is applicable in the context of the linear model, the multi-functional partial linear model, and the multi-functional partial linear single-index model.