Description Usage Arguments Details Value References See Also Examples
Sequential estimation of a regression Dvine for the purpose of quantile prediction as described in Kraus and Czado (2017).
1 2 3 4 5 6 7 8 9 10 11 12 
formula 
an object of class "formula"; same as 
data 
data frame (or object coercible by 
family_set 
see 
selcrit 
selection criterion based on conditional loglikelihood.

order 
the order of covariates in the Dvine, provided as vector of
variable names (after calling

par_1d 
list of options passed to 
weights 
optional vector of weights for each observation. 
cores 
integer; the number of cores to use for computations. 
... 
further arguments passed to 
uscale 
if TRUE, vinereg assumes that marginal distributions have been taken care of in a preliminary step. 
If discrete variables are declared as ordered()
or factor()
, they are
handled as described in Panagiotelis et al. (2012). This is different from
previous version where the data was jittered before fitting.
An object of class vinereg. It is a list containing the elements
the formula used for the fit.
criterion used for variable selection.
the data used to fit the regression model.
list of marginal models fitted by kde1d::kde1d()
.
an rvinecopulib::vinecop_dist()
object containing the fitted
Dvine.
fit statistics such as conditional loglikelihood/AIC/BIC and pvalues for each variable's contribution.
order of the covariates chosen by the variable selection algorithm.
indices of selected variables.
Use
predict.vinereg()
to predict conditional quantiles. summary.vinereg()
shows the contribution of each selected variable with the associated
pvalue derived from a likelihood ratio test.
Kraus and Czado (2017), Dvine copula based quantile regression, Computational Statistics and Data Analysis, 110, 118
Panagiotelis, A., Czado, C., & Joe, H. (2012). Pair copula constructions for multivariate discrete data. Journal of the American Statistical Association, 107(499), 10631072.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  # simulate data
x < matrix(rnorm(200), 100, 2)
y < x %*% c(1, 2)
dat < data.frame(y = y, x = x, z = as.factor(rbinom(100, 2, 0.5)))
# fit vine regression model
(fit < vinereg(y ~ ., dat))
# inspect model
summary(fit)
plot_effects(fit)
# model predictions
mu_hat < predict(fit, newdata = dat, alpha = NA) # mean
med_hat < predict(fit, newdata = dat, alpha = 0.5) # median
# observed vs predicted
plot(cbind(y, mu_hat))
## fixed variable order (no selection)
(fit < vinereg(y ~ ., dat, order = c("x.2", "x.1", "z.1")))

Dvine regression model: y  x.2, x.1
nobs = 100, edf = 2, cll = 82.91, caic = 161.82, cbic = 156.61
var edf cll caic cbic p_value
1 y 0 218.26300 436.5260 436.5260 NA
2 x.2 1 83.39339 164.7868 162.1816 3.723935e38
3 x.1 1 217.78132 433.5626 430.9575 1.000452e96
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Dvine regression model: y  x.2, x.1, z.1
nobs = 100, edf = 2, cll = 82.91, caic = 161.82, cbic = 156.61
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