exp_flexprob: Conditional probability for Exponential distribution

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

Description

exp_flexprob returns the conditional probability P(Y<k | X) of a model fitted via the function exp_flexfit; where λ has been specified to be a function of covariates the required value should be specified using the ‘features’ parameter. The function includes a procedure for visualizing the conditional probability. exp_flexprob also allows for the correlation of estimated parameters via the Cholesky decomposition of the variance-covariance matrix.

Usage

exp_flexprob(K, model, features, visualise = TRUE, xlim, draws = 5)

Arguments

K	Value for which P(Y<k | X) is computed.
model	An object of class "mle2" produced using the function exp_flexfit.
features	A numeric vector specifying the value of covriates at which the conditional probability 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.
visualise	Logical. If TRUE (the default) the conditional distribution is plotted at P(Y<k | x) is shaded.
xlim	Numeric vectors of length 2, giving the coordinate range of the dependent variable.
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 most common parametrization of the Exponential distribution. The probability probability density function is used is:

f(y) = λexp(-λy)

The function returns:

P(Y<k | X) = 1-exp(-kλ)

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

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