fdasrvf (version 1.9.4)

elastic.regression: 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.