lnorm_flexpredict: Conditional mean for Log-Normal distribution

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

Description

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

Usage

lnorm_flexpredict(model, features, draws = 5)

Arguments

model	An object of class "mle2" produced using the function lnorm_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 two parameter parametrization of the Log-Normal distribution. The probability probability density function is used is:

f(y) = [yσ(2π)^1/2]^-1 exp(-log(y-μ)^2/(2σ^2))

The function returns:

E(Y|X) = exp(μ + 0.5σ^2)

μ may be a function of covariates; in which case, the identity link function is used.

References

Faith Ginos, Brenda. "Parameter Estimation For The Lognormal Distribution." Brigham Young University Scholars Archive (2018): 1-111. Web. 10 Aug. 2018.

Kleiber, Christian, and Samuel Kotz. Statistical Size Distributions In Economics And Actuarial Sciences. pp. 107-147. John Wiley & Sons, 2003. Print.

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