devianceBernoulli: Function to calculate deviance for model predictions assuming...

In EdwinGraham/EdNet: Train deep neural networks for classification and regression

Description

This function calculates a deviance measure for model predictions assuming a Bernoulli distribution.

Usage

devianceBernoulli(Y, Y_hat)

Arguments

Y	a numeric vector of observations (0s and 1s.)
Y_hat	a numeric vector of predictions (between 0 and 1) for Y (must have same length as Y.)

Value

a numeric vector.

Author(s)

Edwin Graham <[e-mail]>

Examples

# n <- 1000
# 
# true <- 1/(1+exp(-rnorm(n)))
# observed <- rbinom(n, 1, true)
# predicted <- 1/(1+exp(-(rnorm(n, sd=0.1) + log(true/(1-true))/2 )))
# 
# plot(observed, predicted)
# 
# devs <- devianceBernoulli(observed, predicted)
# sum(devs)

EdwinGraham/EdNet documentation built on May 6, 2019, 12:22 p.m.