gamma_flexpredict: Conditional mean for Gamma distribution

In Shakeel95/bioFlex: Estimation and model selection for strictly positive heavy tailed data, using flexible parametric distributions

Description

gamma_flexpredict returns the conditional mean E(Y|X) of a model fitted via the function gamma_flexfit; where α has been specified to be a function of covariates the required value should be specified using the ‘features’ parameter. gamma_flexpredict also allows for the correlation of estimated parameters via the Cholesky decomposition of the variance-covariance matrix.

Usage

gamma_flexpredict(model, features, draws = 5)

Arguments

model	An object of class "mle2" produced using the function gamma_flexfit.
features	A numeric vector specifying the value of covriates at which the conditional mean should be evaluated; the covariates in the vector should appear in the same order as they do in the model. Where a model does not depend on covariates the argument may be left blank.
draws	The number of random draws from multivariate random normal representing correlated parameters. If parameter correlation is not required draws should be set to zero.

Details

This function uses the the most common parametrization of the Gamma distribution.The probability probability density function is used is:

f(y) = λ^α/Γ(α)•y^α-1 exp(-λy)

The function returns:

E(Y|X) = α/λ

α may be a function of covariates; in which case, the cannonical log link function is used.

References

Kempthorne. "Parameter Estimation Fitting Probability Distributions Method Of Moments." MIT 18.443 (2015)

Shakeel95/bioFlex documentation built on March 3, 2020, 11:27 a.m.