choose.compound {gentransmuted} | R Documentation |
Choose a compound distribution.
Description
choose.compound select a combination of baseline and compounding distributions in the class of compound distribution. See details for supported distributions.
Usage
choose.compound(x, type = "positive", criteria = "AIC")
Arguments
x |
the vector of values to be fitted. |
type |
Support of the x's. Avaliable options: positive (default), unit, real. |
criteria |
model selection criteria to be applied for the selection. Avaliable options: AIC (default, Akaike's information criteria) and BIC (Bayesian's information criteria). |
Details
The compound distribution has cumulative distribution function
F(x;\gamma,\beta,\theta_1,\theta_2)=G_2(G_1(F(x;\gamma,\beta),\theta_1),\theta_2),
where F
is related to the baseline distribution and G_1, G_2
are related to compounding models.
For positive values, the options assessed for F
are exponential, gamma, log-normal, paretoII and Birnbaum-Saunders.
For unit values, the options for F
are beta and Kumaraswamy.
For real values, the options for F
are normal, logistic, Cauchy and Gumbel.
For G_1
and G_2
are assessed all the combinations among the exponentiated, exponentiated of second kind,
Marshall-Olkin, Marshall-Olkin of the second kind and
Value
A list containing the following components:
coefficients |
A matrix with the estimates and standard errors. |
logLik |
The log-likelihood function evaluated in the estimated parameters |
AIC |
Akaike's Information Criterion |
BIC |
Bayesian's Information Criterion |
Author(s)
Yolanda M. Gomez, Diego I. Gallardo, Hector W. Gomez and Barry Arnold
Examples
set.seed(2100)
y=rcompound(100, 1.2, 1.4, 1, 0.8, dist="exp", comp1="EXP", comp2="MO")
choose.compound(y, type="positive")