elastic.lpcr.regression {fdasrvf}

Elastic logistic Principal Component Regression

Description

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

Usage

elastic.lpcr.regression(
  f,
  y,
  time,
  pca.method = "combined",
  no = 5,
  smooth_data = FALSE,
  sparam = 25
)

Arguments

f

matrix (N x M) of M functions with N samples

y

vector of size M labels

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)

Value

Returns a lpcr object containing

alpha

model intercept

b

regressor vector

y

label vector

warp_data

fdawarp object of aligned data

pca

pca object of principal components

Loss

logistic loss

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.