cAIC {phmm} | R Documentation |
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" in the sense 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.
cAIC(object, ..., k = 2)
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. |
Returns a numeric value of the cAIC corresonding to the PHMM fit.
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-.
## Not run: 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) ## End(Not run)