phmm (version 0.4)

cAIC: Conditional Akaike Information Criterion for PHMM

Description

Function calculating the conditional Akaike information criterion (Vaida & Blanchard 2005) for PHMM fitted model objects, according to the formula $-2*log-likelihood + k*rho$, where $rho$ represents the "effective degrees of freedom" of Hodges and Sargent (2001). The function uses the log-likelihood conditional on the estimated random effects; and trace of the "hat matrix", using the generalized linear mixed model formulation of PHMM, to estimate $rho$. The default k = 2, conforms with the usual AIC.

Usage

cAIC(object, ..., k = 2)

Arguments

object

a fitted PHMM model object of class phmm,

...

optionally more fitted model objects.

k

numeric, the penalty per parameter to be used; the default k = 2 conforms with the classical AIC.

Value

• Returns a numeric value of the cAIC corresonding to the PHMM fit.

References

Vaida, F, & Blanchard, S. 2005. Conditional Akaike information for mixed-effects models. Biometrika, 92(2), 351-.

Breslow, NE, Clayton, DG. (1993). Approximate Inference in Generalized Linear Mixed Models. Journal of the American Statistical Association, Vol. 88, No. 421, pp. 9-25.

Whitehead, J. (1980). Fitting Cox's Regression Model to Survival Data using GLIM. Journal of the Royal Statistical Society. Series C, Applied statistics, 29(3), 268-.

Hodges, JS, & Sargent, DJ. 2001. Counting degrees of freedom in hierarchical and other richly-parameterised models. Biometrika, 88(2), 367-.

See Also

phmm, AIC

Examples

data(e1582)
e1582.fit <- phmm(Surv(time, event)~z1+z2+z3+z4+z5+cluster(cluster),
   ~-1+z1, e1582, Gbs = 100, Gbsvar = 1000, VARSTART = 1,
   NINIT = 10, MAXSTEP = 50, CONVERG=40)
summary(e1582.fit)
cAIC(e1582)