scores_logis: Calculating scores for the logistic distribution

In scoringRules: Scoring Rules for Parametric and Simulated Distribution Forecasts

Calculating scores for the logistic distribution

Description

These functions calculate scores (CRPS, logarithmic score) and its gradient and Hessian with respect to the parameters of a location-scale transformed logistic distribution. Furthermore, the censoring transformation and the truncation transformation may be introduced on top of the location-scale transformed logistic distribution.

Usage

## score functions
crps_logis(y, location = 0, scale = 1)
crps_clogis(y, location = 0, scale = 1, lower = -Inf, upper = Inf)
crps_tlogis(y, location = 0, scale = 1, lower = -Inf, upper = Inf)
crps_gtclogis(y, location = 0, scale = 1, lower = -Inf, upper = Inf, lmass = 0, umass = 0)
logs_logis(y, location = 0, scale = 1)
logs_tlogis(y, location = 0, scale = 1, lower = -Inf, upper = Inf)
dss_logis(y, location = 0, scale = 1)

## gradient (location, scale) functions
gradcrps_logis(y, location = 0, scale = 1)
gradcrps_clogis(y, location = 0, scale = 1, lower = -Inf, upper = Inf)
gradcrps_tlogis(y, location = 0, scale = 1, lower = -Inf, upper = Inf)

## Hessian (location, scale) functions
hesscrps_logis(y, location = 0, scale = 1)
hesscrps_clogis(y, location = 0, scale = 1, lower = -Inf, upper = Inf)
hesscrps_tlogis(y, location = 0, scale = 1, lower = -Inf, upper = Inf)

Arguments

y	vector of observations.
location	vector of location parameters.
scale	vector of scale paramters.
lower, upper	lower and upper truncation/censoring bounds.
lmass, umass	vectors of point masses in lower and upper respectively.

Value

For the score functions: a vector of score values.

For the gradient and Hessian functions: a matrix with column names corresponding to the respective partial derivatives.

scoringRules documentation built on Sept. 18, 2024, 5:09 p.m.