elastic.regression: Elastic Linear Regression

In fdasrvf: Elastic Functional Data Analysis

Elastic Linear Regression

Description

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

Usage

elastic.regression(
  f,
  y,
  time,
  B = NULL,
  lam = 0,
  df = 20,
  max_itr = 20,
  smooth_data = FALSE,
  sparam = 25,
  parallel = FALSE,
  cores = 2
)

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
B	matrix defining basis functions (default = NULL)
lam	scalar regularization parameter (default=0)
df	scalar controlling degrees of freedom if B=NULL (default=20)
max_itr	scalar number of iterations (default=20)
smooth_data	smooth data using box filter (default = F)
sparam	number of times to apply box filter (default = 25)
parallel	enable parallel mode using foreach() and doParallel package
cores	set number of cores to use with doParallel (default = 2)

Value

Returns a list containing

alpha	model intercept
beta	regressor function
fn	aligned functions - matrix (N x M) of M functions with N samples
qn	aligned srvfs - similar structure to fn
gamma	warping functions - similar structure to fn
q	original srvf - similar structure to fn
B	basis matrix
b	basis coefficients
SSE	sum of squared errors
type	model type ('linear')

References

Tucker, J. D., Wu, W., Srivastava, A., Elastic Functional Logistic Regression with Application to Physiological Signal Classification, Electronic Journal of Statistics (2014), submitted.

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