getcoef.sigcom: Get the estimated intercept and coefficient functions for...

In FRegSigCom: Functional Regression using Signal Compression Approach

Description

This function is used to calculate the estimates for μ(t), α(t), β_i(s,t) based on the object obtained from cv.sigcom.

Usage

getcoef.sigcom(fit.obj, t.x.coef=NULL, t.y.coef=NULL)

Arguments

fit.obj	the object obtained from cv.sigcom.
t.x.coef	a list of length p of vectors providing the observation time points of predictors on which coefficient functions will be evaluated. If t.x.coef=NULL (default), t.x in cv.sigcom will be used.
t.y.coef	a vector of observation time points of response function on which the coefficient functions will be evaluated. If t.y.coef=NULL (default), t.y in cv.sigcom will be used.

Value

a list containing

mu	the vector of the estimated values of μ(t) at the grid of observation points of the response function (t.y.coef for cv.sigcom).
beta	a list of length p, the number of functional predictors. Its i-th component is a matrix of the estimated values of coefficient functions β_i(s,t) of functional predictors at the full grid of observaion time points created by arguments t.x.coef[[i]] and t.y for cv.sigcom. The columns correspond to different observation points for response varible (t.y.coef).

Author(s)

Ruiyan Luo and Xin Qi

References

Ruiyan Luo and Xin Qi, (2017) Function-on-Function Linear Regression by Signal Compression, Journal of the American Statistical Association. http://www.tandfonline.com/doi/abs/10.1080/01621459.2016.1164053

See Also

cv.sigcom

Examples

#See the examples in cv.sigcom().

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