Scalar-on-Function Regression with Measurement Error Correction
From the summation series of a functional basis to function value
B-splines basis expansion for functional variable data
b-spline basis
b-splines summation series.
Compute the value of the b-splines summation series at certain points.
Extract dimensionality of functional data.
Method of class Fourier_series to extract Fourier coefficients
Extract the value of coefficient parameter function
Solve quantile regression models with functional covariate(s).
Solve linear models with functional covariate(s)
Fourier basis expansion for functional variable data
s4 class of Fourier summation series
Compute the value of the Fourier summation series
Functional principal component basis expansion for functional variable...
Function-valued variable data.
Bias correction method of applying linear regression to one functional...
Bias correction method of applying quantile linear regression to datas...
Bias correction method of applying quantile linear regression to datas...
Use UP_MEM or MP_MEM substitution to apply (generalized) linear regres...
Simulation Data Generation: Scalar-on-function Regression
Simulation Data Generation: Measurement Error Bias Correction of Scala...
Get MEM substitution for (generalized) linear regression with one func...
Numeric basis expansion for functional variable data
Numeric representation of a function basis
Linear combination of a sequence of basis functions represented numeri...
Compute the value of the basis function summation series at certain po...
Pipe operator
Plot b-splines basis summation series.
Plot Fourier basis summation series.
Plot numeric basis function summation series.
Predicted values based on fcQR object
Predicted values based on fcRegression object
Solve scalar-on-function linear models, including generalized linear mixed effect model and quantile linear regression model, and bias correction estimation methods due to measurement error. Details about the measurement error bias correction methods, see Luan et al. (2023) <doi:10.48550/arXiv.2305.12624>, Tekwe et al. (2022) <doi:10.1093/biostatistics/kxac017>, Zhang et al. (2023) <doi:10.5705/ss.202021.0246>, Tekwe et al. (2019) <doi:10.1002/sim.8179>.