compute_pairwise_norm_scalar: Title Compute scalar normalised pairwise distances

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

Title Compute scalar normalised pairwise distances

Description

Title Compute scalar normalised pairwise distances

Usage

compute_pairwise_norm_scalar(
  .data,
  gran_x = NULL,
  gran_facet = NULL,
  response = NULL,
  quantile_prob = seq(0.01, 0.99, 0.01),
  dist_ordered = TRUE,
  lambda = 0.67
)

Arguments

.data	data for which mmpd needs to be calculated
gran_x	granularities mapped across x levels
gran_facet	granularities mapped across facetss
response	univarite response variable
quantile_prob	probabilities
dist_ordered	if categories are ordered
lambda	value of tuning parameter for computing weighted pairwise distances

Value

the weighted pairwise distance normalised through modeling raw distances as a function of total number of categories

Examples

library(dplyr)
library(parallel)
sm <- smart_meter10 %>%
  dplyr::filter(customer_id %in% c("10017936"))
gran_x <- "day_week"
gran_facet <- "month_year"
v <- compute_pairwise_norm_scalar(sm, gran_x, gran_facet,
  response = general_supply_kwh, lambda = 0.67
)
# month of the year not working in this setup

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