compute_quantiles: compute quantiles of data across categories

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

compute quantiles of data across categories

Description

compute quantiles of data across categories

Usage

compute_quantiles(sim_panel_data, quantile_prob = seq(0.01, 0.99, 0.01))

Arguments

sim_panel_data	data with categories and response variable
quantile_prob	quantiles of reponse variable needed

Value

data with quantiles of response variable corresponding to categories

Author(s)

Sayani07

Examples

library(ggplot2)
library(dplyr)
library(distributional)
library(tidyr)
sim_panel_data <- sim_panel(
  nx = 3,
  nfacet = 2,
  ntimes = 100,
  sim_dist = distributional
  ::dist_normal(5, 10)
) %>%
  unnest(c(data))
sim_panel_data %>% ggplot() +
  geom_boxplot(aes(x = as.factor(id_x), y = sim_data)) +
  facet_wrap(~id_facet)
compute_quantiles(sim_panel_data) %>%
  unnest(sim_data_quantile) %>%
  ggplot() +
  geom_boxplot(aes(x = as.factor(id_x), y = unlist(sim_data_quantile))) +
  facet_wrap(~id_facet)

sim_varx_normal <- function(nx, nfacet, mean, sd, w) {
  rep(dist_normal((mean + seq(0, nx - 1, by = 1) * w), sd), nfacet)
}
data <- sim_panel(
  nx = 3,
  nfacet = 2,
  ntimes = 100,
  sim_dist = sim_varx_normal(nx = 3, nfacet = 2, mean = 0, sd = 1, w = 100)
)
sim_panel_data %>% ggplot() +
  geom_boxplot(aes(x = as.factor(id_x), y = sim_data)) +
  facet_wrap(~id_facet)
compute_quantiles(sim_panel_data) %>%
  unnest(sim_data_quantile) %>%
  ggplot() +
  geom_boxplot(aes(x = as.factor(id_x), y = unlist(sim_data_quantile))) +
  facet_wrap(~id_facet)

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