outsample_sim: !!!!Achtung: Weighted methods are not done and are omitted....

In swihart/wfpca: weighted functional prinicpal components analysis

Description

This function comes after many months of running readme_sim and talks with Bryan Lau on what we need to demonstrate for the method.

Usage

outsample_sim(sim_seed = 101, sample_size = 1000, sample_size2 = 1,
  sim_slope = 100, sim_intercept = 12, sim_ses_coef = 0.01,
  sim_age_coef = 0.01)

Arguments

sim_seed	passed to set.seed()
sample_size	(defaults to 1000) and must be a multiple of 100
sample_size2	(defaults to 1) the out of sample sample size
sim_slope	see slope in calculate_ses()
sim_intercept	see intercept in calculate_ses()
sim_ses_coef	see ses_coef in apply_censoring()
sim_age_coef	see age_coef in apply_censoring()

Value

results a data frame with rmse for each approach for that simulated dataset

Examples

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swihart/wfpca documentation built on May 30, 2019, 10:38 p.m.