This file contains the source code of an exemplary application of the D-vine copula based quantile regression approach implemented in the R-package *vinereg* and presented in Kraus and Czado (2017): *D-vine copula based quantile regression*, Computational Statistics and Data Analysis, 110, 1-18. Please, feel free to address questions to daniel.kraus@tum.de.

We consider the data set `abalone`

from the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/abalone) and focus on the female sub-population. In a first application we only consider continuous variables and fit models to predict the quantiles of weight (`whole`

) given the predictors `length`

, `diameter`

, and `height`

.

We consider the female subset and fit a parametric regression D-vine for the response weight given the covariates len, diameter and height (ignoring the discreteness of some of the variables). The D-vine based model is selected sequentially by maximizing the conditional log-likelihood of the response given the covariates. Covariates are only added if they increase the (possibly AIC- or BIC-corrected) conditional log-likelihood.

We use the function `vinereg()`

to fit a regression D-vine for predicting the response weight given the covariates `length`

, `diameter`

, and `height`

. The argument `family_set`

determines how the pair-copulas are estimated. We will only use one-parameter families and the *t* copula here. The `selcrit`

argument specifies the penalty type for the conditional log-likelihood criterion for variable selection.

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

The result has a field `order`

that shows the selected covariates and their ranking order in the D-vine.

The field `vine`

contains the fitted D-vine, where the first variable corresponds to the response. The object is of class `"vinecop_dist"`

so we can use `rvineocpulib`

’s functionality to summarize the model

We can also plot the contours of the fitted pair-copulas.

In order to visualize the predicted influence of the covariates, we plot the estimated quantiles arising from the D-vine model at levels 0.1, 0.5 and 0.9 against each of the covariates.

We call the `fitted()`

function on `fit_vine_par`

to extract the fitted values for multiple quantile levels. This is equivalent to predicting the quantile at the training data. The latter function is more useful for out-of-sample predictions.

```
## 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)
```

To examine the effect of the individual variables, we will plot the predicted quantiles against each of the variables. To visualize the relationship more clearly, we add a smoothed line for each quantile level. This gives an estimate of the expected effect of a variable (taking expectation with respect to all other variables).

The fitted quantile curves suggest a non-linear effect of all three variables.

This can be compared to linear quantile regression:

```
## 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)
```

We also want to check whether these results change, when we estimate the pair-copulas nonparametrically.

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

Now only the length and height variables are selected as predictors. Let’s have a look at the marginal effects.

The effects look similar to the parametric one, but slightly more wiggly. Note that even the diameter was not selected as a covariate, it’s marginal effect is captured by the model. It just does not provide additional information when height and length are already accounted for.

To deal with discrete variables, we use the methodology of Schallhorn et al. (2017). For estimating nonparametric pair-copulas with discrete variable(s), jittering is used (Nagler, 2017).

We let `vinereg()`

know that a variable is discrete by declaring it `ordered`

.

Kraus and Czado (2017), **D-vine copula based quantile regression**, *Computational Statistics and Data Analysis, 110, 1-18*

Nagler (2017), **A generic approach to nonparametric function estimation with mixed data**, *Statistics & Probability Letters, 137:326–330*

Schallhorn, Kraus, Nagler and Czado (2017), **D-vine quantile regression with discrete variables**, *arXiv preprint*