veesa (version 0.1.7)

plot_pc_diffs: Plot principal component directions

Description

Function for plotting the functional PC directions

Usage

plot_pc_diffs(
  fpcs,
  fdasrvf,
  fpca_method,
  times = NULL,
  digits = 0,
  alpha = 1,
  alpha_fill = 0.5,
  nrow = 1,
  linesizes = NULL,
  mean_linesize = NULL,
  linetype = TRUE,
  freey = FALSE
)

Value

ggplot2 plot of specified differences beteen principal component directions and the Karcher mean

Arguments

fpcs

Vector of numbers identifying the PCs to include in the plot

fdasrvf

Object output from jointFPCA, horizFPCA, or vertFPCA

fpca_method

Character string specifying the type of elastic fPCA method to use ('jfpca', 'hfpca', or 'vfpca')

times

Optional vector of times (if not included, times will be represented on the interval from 0 to 1)

digits

Number of digits to print in the title for the proportion of variability explained by a PC

alpha

Vector of alpha values associated with lines in plot (length must match number of lines in plot)

alpha_fill

Value of alpha to use with the fill color in the ribbons (length of 1).

nrow

Number of rows to use when creating a grid of plots

linesizes

Vector of line widths associated with lines in plot (length must match number of lines in plot)

mean_linesize

Value of width to use with the horizontal line with an intercept of 0.

linetype

Vector of line types (e.g., "solid" or "dashed") associated with lines in plot (length must match number of lines in plot)

freey

Indicator for whether y-axis should be freed across facets

Examples

# Load packages
library(dplyr)
library(tidyr)

# Select a subset of functions from shifted peaks data
sub_ids <-
  shifted_peaks$data |>
  select(data, group, id) |>
  distinct() |>
  group_by(data, group) |>
  slice(1:4) |>
  ungroup()

# Create a smaller version of shifted data
shifted_peaks_sub <-
  shifted_peaks$data |>
  filter(id %in% sub_ids$id)

# Extract times
shifted_peaks_times = unique(shifted_peaks_sub$t)

# Convert training data to matrix
shifted_peaks_train_matrix <-
  shifted_peaks_sub |>
  filter(data == "Training") |>
  select(-t) |>
  mutate(index = paste0("t", index)) |>
  pivot_wider(names_from = index, values_from = y) |>
  select(-data, -id, -group) |>
  as.matrix() |>
  t()

# Obtain veesa pipeline training data
veesa_train <-
  prep_training_data(
    f = shifted_peaks_train_matrix,
    time = shifted_peaks_times,
    fpca_method = "jfpca"
  )

# Plot principal directions of PC1
plot_pc_diffs(
  fpcs = 1,
  fdasrvf = veesa_train$fpca_res,
  fpca_method = "jfpca",
  times = -shifted_peaks_times,
  linesizes = rep(0.75,5),
  mean_linesize = 0.5,
  alpha = 0.9,
  alpha_fill = 0.25
 )