copulareg {copulareg}

copulareg

Description

This function fits joint distributions with an R-vine pair copula structure, that is constructed in a specific way so that the conditional density and distribution of the variable y can be computed explicitly.

Usage

copulareg.default(
  y,
  x,
  var_type_y,
  var_type_x,
  family_set = c("gaussian", "clayton", "gumbel"),
  extra_x = NULL,
  extra_y = NULL
)

Arguments

y

A vector of n observations of the (univariate) outcome variable y

x

A (n x p) matrix of n observations of p covariates

var_type_y

A character that has to be specified as "d" or "c" to indicate whether y is discrete or continuous, respectively.

var_type_x

A vector of p characters that have to take the value "c" or "d" to indicate whether each margin of the covariates is discrete or continuous.

family_set

A vector of strings that specifies the set of pair-copula families that the fitting algorithm chooses from. For an overview of which values that can be specified, see the documentation for bicop.

extra_x

Optional extra values of x to use for estimating the margins of the covariates.

extra_y

Optional extra values of y to use to estimate the margin of y.

Value

An object of the class 'copulareg', which contains an object of the class 'vinecop', see rvinecopulib::vinecop, an enviroment that stores the transformed variables at each level of the pair copula for the training data, the marginal distributions of the covariates, the marginal distribution of the response, and the y-training values.

Examples

# Compile some test data
data('ChickWeight') 
set.seed(10)
tr <- sample(c(TRUE, FALSE), nrow(ChickWeight), TRUE, c(0.7, 0.3))
y_tr <- ChickWeight$weight[tr]
y_te <- ChickWeight$weight[!tr]
x_tr <- apply(ChickWeight[tr, -1], 2, as.numeric)
x_te <- apply(ChickWeight[!tr, -1], 2, as.numeric)
var_type_x <- apply(x_tr, 2,
                    function(x) if(length(unique(x)) < 10) "d" else "c")

# Fit model to training data
md <- copulareg::copulareg(y_tr, x_tr, "c", var_type_x)

# Predict for a new data matrix
pred <- predict(md, new_x = x_te)

# Plot residuals for test data against covariates
plot(data.frame(residual = y_te - pred, x_te))

# Plot residuals against fitted
plot(md)

# Plot prediction error against predicted values
plot(md, new_x=x_te, new_y=y_te)