# install.packages("devtools")
devtools::install_github("seonjoo/HDLFPCA")

library(HDLFPCA)
example_hd_data = generate_hd_data(I = 100)
phix0 = example_hd_data$phix0
phix1 = example_hd_data$phix1
phiw = example_hd_data$phiw
Y = example_hd_data$Y
time = example_hd_data$time
J = example_hd_data$J
I = example_hd_data$I
visit = example_hd_data$visit
re <- hd_lfpca(
    Y,
    T = scale(time, center = TRUE, scale = TRUE),
    J = J,
    I = I,
    visit = visit,
    varthresh = 0.95,
    projectthresh = 1,
    timeadjust = FALSE,
    figure = TRUE
  )
#> Reduce Dimension
#> Estimate Covariance Functions
#> Calcuate Scores
#> Compute Residual to the Demeaned Data.
#> Residual of LFPCA Model is: 9872.67284871758

cor(phix0, re$phix0)
#>             [,1]        [,2]        [,3]        [,4]
#> [1,]  0.99720326  0.09083731  0.02831191 -0.03210385
#> [2,]  0.04360455 -0.99192254  0.03077443  0.07037896
#> [3,] -0.02504620  0.01250388 -0.97917094  0.11347137
cor(phix1, re$phix1)
#>             [,1]        [,2]         [,3]       [,4]
#> [1,]  0.99755948  0.08022781 -0.001390277 0.12272830
#> [2,]  0.04494263 -0.98371159  0.029040865 0.05042425
#> [3,] -0.02278886  0.01911020 -0.993862522 0.07308564

library(gplots)
#> 
#> Attaching package: 'gplots'
#> The following object is masked from 'package:stats':
#> 
#>     lowess
par(mfrow = c(2, 2),
    mar = rep(0.5, 4),
    bg = "gray")
bs = c(-100:100) / 1000 * 1.5

image(phix0,
      axes = FALSE,
      col = gplots::bluered(200),
      breaks = bs)
image(re$phix0[, 1:3],
      axes = FALSE,
      col = gplots::bluered(200),
      breaks = bs)
image(phix1,
      axes = FALSE,
      col = gplots::bluered(200),
      breaks = bs)
image(re$phix1[, 1:3],
      axes = FALSE,
      col = gplots::bluered(200),
      breaks = bs)