Extract Features by Functional Principal Component Analysis (FPCA)

Source: R/FPCA_related.R

Performs FPCA to extract features from longitudinal encounter data.

fpca.combine(
  time_code,
  follow_up_time,
  K.select = "PropVar",
  n.grid = 401,
  propvar = 0.85
)

Arguments

time_code

longitudinal encounter times. See an example as longitudinal data.
follow_up_time

follow-up time information. See an example as follow_up_time.
K.select

characters indicating which method to choose the number of principal components K. Default is K.select="PropVar", and K.select="PPIC" is also available.
n.grid

an integer value for grid points used in estimating covariance function g. Default is 401.
propvar

a proportion of variation used to select number of FPCs. Default is 0.85.