R: Elastic Logistic Regression

elastic.logistic {fdasrvf}

R Documentation

Elastic Logistic Regression

Description

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

Usage

elastic.logistic(
  f,
  y,
  time,
  B = NULL,
  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 labels (1/-1)
`time`	vector of size N describing the sample points
`B`	matrix defining basis functions (default = NULL)
`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
`Loss`	logistic loss
`type`	model type ('logistic')

References

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

[Package fdasrvf version 1.9.7 Index]