Description Usage Arguments Details References
lomax_flexprob returns the conditional probability P(Y<k | X) of a model fitted via the function lomax_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. lomax_flexprob also allows for the correlation of estimated parameters via the Cholesky decomposition of the variance-covariance matrix.
1 | lomax_flexprob(K, model, features, visualise = TRUE, xlim, draws = 5)
|
K |
Value for which P(Y<k | X) is computed. |
model |
An object of class "mle2" produced using the function lomax_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. |
This function uses the same parametrization of the Lomax distribution as is used in Kleiber and Kotz (2003). The probability probability density function is used is:
f(y) = (α/λ) [1 + (x/λ)]^-(α+1)
The function returns:
P(Y>k | X) = 1-[1+k/λ]^-α
λ may be a function of covariates; in which case, the cannonical log link function is used.
Kleiber, Christian, and Samuel Kotz. Statistical Size Distributions In Economics And Actuarial Sciences. pp. 107-147. John Wiley & Sons, 2003. Print.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.