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
)

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')

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)

References

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