Description Usage Arguments Details References
betapr_flexprob returns the conditional probability P(Y<k | X) of a model fitted via the function betapr_flexfit; where α 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. betapr_flexprob also allows for the correlation of estimated parameters via the Cholesky decomposition of the variance-covariance matrix.
1 | betapr_flexprob(K, model, features, visualise = TRUE, draws = 5, xlim)
|
K |
Value for which P(Y<k | X) is computed. |
model |
An object of class "mle2" produced using the function betapr_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. |
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. |
xlim |
Numeric vectors of length 2, giving the coordinate range of the dependent variable. |
This function uses the two parameter parametrization of the Beta Prime distribution is used in Johnson and Kotz (1995). The tow parameter distribution ins a special case of the three parameter distribution, with σ = 1. The probability probability density function is used is:
f(y) = [y^α-1 (1+y)^-(α+β)]/Β(α,β)
The function returns:
P(Y<k | X) = I(α,β)
α may be a function of covariates; in which case, the cannonical log link function is used.
Johnson, N.L., Kotz, S., Balakrishnan, N. (1995). Continuous Univariate Distributions, Volume 2 (2nd Edition), Wiley.
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