getcoef.nonlinear: Get the estimated intercept and nonlinear functions in...

In FRegSigCom: Functional Regression using Signal Compression Approach

Description

This function is used to calculate the estimates for μ(t), F_i(x,s,t)'s based on the object obtained from cv.nonlinear.

Usage

getcoef.nonlinear(fit.cv, n.x.grid = 50)

Arguments

fit.cv	the object obtained from cv.nonlinear.
n.x.grid	the number of grid points of x. The estimated F_i(x,s,t) is calculated in a three-dimensional grid of (x,s,t). The grid points of s and t are the observation points of X_i(s) and Y(t) used in cv.nonlinear, respectively. The grid of x includes n.x.grid equally spaced values between the minimum and maximum of all the discretely observed values of X_i(s). Default of n.x.grid is 50.

Value

a list containing

mu	the vector of estimated values of μ(t) at the observation points of the response function.
F	a list of length p, the number of functional predictors. Its i-th element is a three dimensional array with estimated values of F_i(x,s,t) on the three-dimensional grid X.grid[[i]]*t.x.list[[i]]*t.y (see below).
X.grid	a list of length p. Its i-th element is the vector of grid points for x and includes n.x.grid equally spaced values between the minimum and maximum of all the discretely observed values of X_i(s).
t.x.list	one of the arguments in cv.nonlinear, specifying the list of the vectors of obesrvation points for X_i(s), 1≤ i≤ p.
t.y	one of the arguments in cv.nonlinear, specifying the vector of obesrvation points of the response curve Y(t).

Author(s)

Ruiyan Luo and Xin Qi

See Also

cv.nonlinear.

Examples

#See the examples in cv.nonlinear().

FRegSigCom documentation built on May 1, 2019, 9:45 p.m.