fdasrvf (version 2.3.6)

elastic.mlpcr.regression: Elastic Multinomial logistic Principal Component Regression

Description

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

Usage

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

Value

Returns a mlpcr object containing

alpha

model intercept

b

regressor vector

y

label vector

Y

Coded labels

warp_data

fdawarp object of aligned data

pca

pca object of principal components

Loss

logistic loss

pca.method

string specifying pca method used

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)

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.