distance_all_pairwise: computing all within and between facet distances between...

In Sayani07/gravitas: Explore Probability Distributions for Bivariate Temporal Granularities

computing all within and between facet distances between quantile categories given quantile data

Description

computing all within and between facet distances between quantile categories given quantile data

Usage

distance_all_pairwise(
  sim_panel_quantiles,
  quantile_prob = seq(0.01, 0.99, 0.01),
  dist_ordered = TRUE,
  lambda = 0.67
)

Arguments

sim_panel_quantiles	quantile data
quantile_prob	numeric vector of probabilities with value #'in [0,1] whose sample quantiles are wanted. Default is set to #' "decile" plot
dist_ordered	if categories are ordered
lambda	value of tuning parameter for computing weighted pairwise distances

Value

within and between facet distances

Author(s)

Sayani07

Examples

library(dplyr)
library(parallel)
library(ggplot2)
library(distributional)
library(tidyr)
sim_panel_data <- sim_panel(
  nx = 2,
  nfacet = 3,
  ntimes = 5,
  sim_dist = distributional
  ::dist_normal(5, 10)
) %>% tidyr::unnest(c(data))
sim_panel_quantiles <-
  compute_quantiles(sim_panel_data)

distance_all_pairwise(sim_panel_quantiles, lambda = 0.5)
dist_data <- distance_all_pairwise(sim_panel_quantiles, lambda = 0.7)
# Plot raw distances
ggplot(dist_data, aes(x = 1:9, y = value, colour = dist_type)) +
  geom_line() +
  geom_point()
# Plot transformed distances
ggplot(dist_data, aes(
  x = 1:9, y = trans_value, colour =
    dist_type
)) +
  geom_line() +
  geom_point()

Sayani07/gravitas documentation built on June 18, 2022, 2:40 a.m.