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fdasrvf (version 2.3.6)

elastic.pcr.regression: Elastic Linear Principal Component Regression

Description

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

Usage

elastic.pcr.regression(
  f,
  y,
  time,
  pca.method = "combined",
  no = 5,
  smooth_data = FALSE,
  sparam = 25,
  parallel = F,
  C = NULL
)

Value

Returns a pcr object containing

alpha

model intercept

b

regressor vector

y

response vector

warp_data

fdawarp object of aligned data

pca

pca object of principal components

SSE

sum of squared errors

pca.method

string specifying pca method used

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

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)

parallel

run in parallel (default = F)

C

scale balance parameter for combined method (default = NULL)

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.