predict_hpaBinary

object

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

logical; if TRUE (default) then function returns predicted probabilities. Otherwise latent variable
(single index) estimates will be returned.

is_prob

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_hpaBinary 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_hpaBinary: Predict method for hpaBinary

Description

Usage

Arguments

Value