## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, warning = FALSE)

## ---- message = FALSE---------------------------------------------------------
library(vinereg) 
require(ggplot2)
require(dplyr)
require(tidyr)
require(AppliedPredictiveModeling)

## -----------------------------------------------------------------------------
set.seed(5)

## -----------------------------------------------------------------------------
data(abalone, package = "AppliedPredictiveModeling")
colnames(abalone) <- c(
  "sex", "length", "diameter", "height", "whole", 
  "shucked", "viscera", "shell", "rings"
)
abalone_f <- abalone %>%
    dplyr::filter(sex == "F") %>%        # select female abalones
    dplyr::select(-sex) %>%         # remove id and sex variables
    dplyr::filter(height < max(height))  # remove height outlier

## ---- fig.width=7, fig.height=6-----------------------------------------------
pairs(abalone_f, pch = ".")

## -----------------------------------------------------------------------------
fit_vine_par <- vinereg(
  whole ~ length + diameter + height, 
  data = abalone_f,  
  family_set = c("onepar", "t"),
  selcrit = "aic"
)

## -----------------------------------------------------------------------------
fit_vine_par$order

## -----------------------------------------------------------------------------
summary(fit_vine_par$vine)

## ---- fig.width=7, fig.height=7-----------------------------------------------
contour(fit_vine_par$vine)

## -----------------------------------------------------------------------------
# quantile levels
alpha_vec <- c(0.1, 0.5, 0.9) 

## -----------------------------------------------------------------------------
pred_vine_par <- fitted(fit_vine_par, alpha = alpha_vec)
# equivalent to:
# predict(fit_vine_par, newdata = abalone.f, alpha = alpha_vec)
head(pred_vine_par)

## ---- fig.width=7, fig.height=4-----------------------------------------------
plot_effects(fit_vine_par)

## ---- fig.width=7, fig.height=6-----------------------------------------------
pred_lqr <- pred_vine_par
for (a in seq_along(alpha_vec)) {
    my.rq <- quantreg::rq(
        whole ~ length + diameter + height, 
        tau = alpha_vec[a], 
        data = abalone_f
    )
    pred_lqr[, a] <- quantreg::predict.rq(my.rq)
}

plot_marginal_effects <- function(covs, preds) {
    cbind(covs, preds) %>%
        tidyr::gather(alpha, prediction, -seq_len(NCOL(covs))) %>%
        dplyr::mutate(prediction = as.numeric(prediction)) %>%
        tidyr::gather(variable, value, -(alpha:prediction)) %>%
        ggplot(aes(value, prediction, color = alpha)) +
        geom_point(alpha = 0.15) + 
        geom_smooth(method = "gam", formula = y ~ s(x, bs = "cs"), se = FALSE) + 
        facet_wrap(~ variable, scale = "free_x") +
        ylab(quote(q(y* "|" * x[1] * ",...," * x[p]))) +
        xlab(quote(x[k])) +
        theme(legend.position = "bottom")
}
plot_marginal_effects(abalone_f[, 1:3], pred_lqr)

## ---- fig.width=4.6, fig.height=4.6-------------------------------------------
fit_vine_np <- vinereg(
  whole ~ length + diameter + height,
  data = abalone_f,
  family_set = "nonpar",
  selcrit = "aic"
)
fit_vine_np
contour(fit_vine_np$vine)

## ---- fig.width=7, fig.height=4-----------------------------------------------
plot_effects(fit_vine_np, var = c("diameter", "height", "length"))

## ---- fig.width=4.7, fig.height=4---------------------------------------------
abalone_f$rings <- as.ordered(abalone_f$rings)
fit_disc <- vinereg(rings ~ ., data = abalone_f, selcrit = "aic")
fit_disc
plot_effects(fit_disc)

