## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7.5,
  fig.height = 4.8,
  message = FALSE,
  warning = FALSE
)

## -----------------------------------------------------------------------------
library(wqrr)

## -----------------------------------------------------------------------------
dat <- read.csv(system.file("extdata", "wqrr_data.csv", package = "wqrr"))
str(dat)
head(dat)

## -----------------------------------------------------------------------------
dat <- tail(dat, 192)

## -----------------------------------------------------------------------------
wqr_fit <- wavelet_qr(
  y         = dat$sp500_return,
  x         = dat$oil_return,
  quantiles = seq(0.1, 0.9, by = 0.1),
  wavelet   = "la8",
  J         = 5,
  verbose   = FALSE
)
print(wqr_fit)

## -----------------------------------------------------------------------------
wqr_to_matrix(wqr_fit)

## ----eval = FALSE-------------------------------------------------------------
# plot_wqr_heatmap(wqr_fit, colorscale = "Parula")

## ----eval = FALSE-------------------------------------------------------------
# plot(wqr_fit)

## -----------------------------------------------------------------------------
results_table(wqr_fit, digits = 3)

## -----------------------------------------------------------------------------
mwqr_fit <- multivariate_wqr(
  y         = dat$sp500_return,
  X_list    = list(oil = dat$oil_return, epu = dat$epu),
  quantiles = c(0.10, 0.25, 0.50, 0.75, 0.90),
  wavelet   = "la8",
  J         = 5,
  verbose   = FALSE
)
print(mwqr_fit)

## ----eval = FALSE-------------------------------------------------------------
# plot(mwqr_fit, variable = "oil", colorscale = "Parula")

## -----------------------------------------------------------------------------
wqqr_fit <- wavelet_qqr(
  y             = dat$sp500_return,
  x             = dat$oil_return,
  quantile_step = 0.10,
  wavelet       = "la8",
  J             = 5,
  band          = "long",
  verbose       = FALSE
)
print(wqqr_fit)

## ----eval = FALSE-------------------------------------------------------------
# plot(wqqr_fit, type = "3d", colorscale = "Parula")
# plot(wqqr_fit, type = "pvalue")
# plot(wqqr_fit, type = "compare")

## -----------------------------------------------------------------------------
cause_fit <- np_quantile_causality(
  x         = dat$oil_return,
  y         = dat$sp500_return,
  test_type = "mean",
  q         = seq(0.1, 0.9, by = 0.1)
)
print(cause_fit)

## ----eval = FALSE-------------------------------------------------------------
# plot(cause_fit)

## -----------------------------------------------------------------------------
wcr_fit <- wavelet_np_causality(
  x       = dat$oil_return,
  y       = dat$sp500_return,
  q       = seq(0.1, 0.9, by = 0.1),
  wavelet = "la8",
  J       = 5,
  verbose = FALSE
)
print(wcr_fit)

## ----eval = FALSE-------------------------------------------------------------
# plot(wcr_fit, colorscale = "Parula")

## -----------------------------------------------------------------------------
med_fit <- wavelet_mediation(
  y         = dat$sp500_return,
  x         = dat$oil_return,
  z         = dat$epu,
  quantiles = c(0.10, 0.25, 0.50, 0.75, 0.90),
  wavelet   = "la8",
  J         = 5,
  dep_name  = "SP500",
  main_name = "OIL",
  mod_name  = "EPU",
  verbose   = FALSE
)
print(med_fit)

## ----eval = FALSE-------------------------------------------------------------
# panels <- plot_mediation_panel(med_fit, colorscale = "Parula")
# panels$Direct
# panels$Indirect

## -----------------------------------------------------------------------------
wc_fit <- wavelet_quantile_correlation(
  x         = dat$oil_return,
  y         = dat$sp500_return,
  quantiles = c(0.10, 0.25, 0.50, 0.75, 0.90),
  wavelet   = "la8",
  J         = 5,
  n_sim     = 100,
  verbose   = FALSE
)
print(wc_fit)
head(wc_fit$results)

## ----eval = FALSE-------------------------------------------------------------
# plot(wc_fit, colorscale = "Parula")

## -----------------------------------------------------------------------------
qd_fit <- wavelet_quantile_density(dat$sp500_return, j0 = 4, bandwidth = 0.15)
print(qd_fit)

## ----eval = FALSE-------------------------------------------------------------
# plot(qd_fit)

## -----------------------------------------------------------------------------
wqrr_colorscales(show_preview = TRUE)

## ----fig.height = 1.2---------------------------------------------------------
op <- par(mar = c(0, 0, 0, 0))
image(matrix(1:256, ncol = 1), col = parula_colors(256), axes = FALSE)
par(op)