predict.hpaML

object

further arguments (currently ignored)

An optional data frame (for <a rd-options="hpa" href="/link/hpaBinary?package=hpa&version=1.0.1&to=hpa" data-mini-rdoc="hpa::hpaBinary">hpaBinary</a> and <a rd-options="hpa" href="/link/hpaSelection?package=hpa&version=1.0.1&to=hpa" data-mini-rdoc="hpa::hpaSelection">hpaSelection</a>) 
or numeric matrix (for <a rd-options="hpa" href="/link/hpaML?package=hpa&version=1.0.1&to=hpa" data-mini-rdoc="hpa::hpaML">hpaML</a>) in which to look for variables 
with which to predict. If omitted, the original dataframe (matrix) used.

newdata

Multivariate conditional and marginal densities, moments, cumulative distribution functions as well as binary choice and sample selection models based on hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) <doi:10.2307/1913241>.

Potanin Bogdan

Distributions Hermite Polynomial Approximation

predict.hpaML function

An optional data frame (for <a rd-options='hpa' href='hpaBinary'>hpaBinary</a> and <a rd-options='hpa' href='hpaSelection'>hpaSelection</a>) 
or numeric matrix (for <a rd-options='hpa' href='hpaML'>hpaML</a>) in which to look for variables 
with which to predict. If omitted, the original dataframe (matrix) used.

predict.hpaML: Predict method for hpaML

Description

Usage

Arguments

Value