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fdasrvf (version 1.9.4)

elastic.pcr.regression: Elastic Linear Prinipcal 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
)

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 specifing pca method (options = "combined", "vert", or "horiz", default = "combined")

no

scalar specifify 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)

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 specifing pca method used

References

J. D. Tucker, J. R. Lewis, and A. Srivastava, <U+201C>Elastic Functional Principal Component Regression,<U+201D> Statistical Analysis and Data Mining, 10.1002/sam.11399, 2018.