elastic.pcr.regression: Elastic Linear Principal Component Regression

In fdasrvf: Elastic Functional Data Analysis

Elastic Linear Principal Component Regression

Description

This function identifies a regression model with phase-variability using elastic pca

Usage

elastic.pcr.regression(
  f,
  y,
  time,
  pca.method = "combined",
  no = 5,
  smooth_data = FALSE,
  sparam = 25,
  parallel = F,
  C = NULL
)

Arguments

f	matrix (N x M) of M functions with N samples
y	vector of size M responses
time	vector of size N describing the sample points
pca.method	string specifying pca method (options = "combined", "vert", or "horiz", default = "combined")
no	scalar specify number of principal components (default = 5)
smooth_data	smooth data using box filter (default = F)
sparam	number of times to apply box filter (default = 25)
parallel	run in parallel (default = F)
C	scale balance parameter for combined method (default = NULL)

Value

Returns a pcr object containing

alpha	model intercept
b	regressor vector
y	response vector
warp_data	fdawarp object of aligned data
pca	pca object of principal components
SSE	sum of squared errors
pca.method	string specifying pca method used

References

J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic Functional Principal Component Regression,” Statistical Analysis and Data Mining, 10.1002/sam.11399, 2018.

fdasrvf documentation built on Nov. 19, 2023, 1:09 a.m.