select_harmonies: Select harmonies with significant patterns

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

Select harmonies with significant patterns

Description

Select harmonies with significant patterns

Usage

select_harmonies(
  .data,
  harmony_tbl = NULL,
  response = NULL,
  quantile_prob = seq(0.01, 0.99, 0.01),
  dist_ordered = TRUE,
  lambda = 0.67,
  nperm = 200,
  use_perm = TRUE,
  nsamp = 200
)

Arguments

.data	a tsibble or data with already computed categories
harmony_tbl	A tibble containing one or more hamronies with facet_variable, x_variable, facet_levels and x_levels
response	the response variable
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
nperm	number of permutations for normalization
use_perm	should permutation approach for normalization be used
nsamp	number of permutation for computing the threshold

Examples

## Not run: 
library(parallel)
library(dplyr)
library(tidyr)
sm <- smart_meter10 %>%
  filter(customer_id %in% c("10017994"))
harmonies <- sm %>%
  harmony(
    ugran = "year",
    filter_in = "wknd_wday",
    filter_out = c("hhour", "fortnight", "quarter", "semester")
  )
harmonies1 <- harmonies %>% mutate(facet_variable = NA)
h <- harmonies1 %>%
  select(-facet_levels) %>%
  distinct() %>%
  dplyr::mutate(facet_levels = NA)
all_harmony <- select_harmonies(sm,
  harmony_tbl = h,
  response = general_supply_kwh, nperm = 200, nsamp = 20
)
all_harmony2 <- select_harmonies(sm,
  harmony_tbl = harmonies,
  response = general_supply_kwh, nperm = 20, nsamp = 20
)

## End(Not run)

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