objfun | R Documentation |
Get the contributions of an objective function. For glm
these are the (weighted) log-likelihood contributions, for lm
the
negative (weighted) squared error.
objfun(x, ...)
## S3 method for class 'survreg'
objfun(x, newdata = NULL, weights = NULL, ...)
## S3 method for class 'lm'
objfun(x, newdata = NULL, weights = NULL, ...)
## S3 method for class 'glm'
objfun(x, newdata = NULL, weights = NULL, log = TRUE, ...)
x |
model object. |
... |
further arguments passed on to |
newdata |
optional. New data frame. Can be useful for model evaluation / benchmarking. |
weights |
optional. Prior weights. See |
log |
should the log-Likelihood contributions or the Likelhood contributions be returned? |
vector of objective function contributions.
## Example taken from ?stats::glm
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
print(d.AD <- data.frame(treatment, outcome, counts))
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
logLik_contributions <- objfun(glm.D93)
sum(logLik_contributions)
logLik(glm.D93)
if(require("survival")) {
x <- survreg(Surv(futime, fustat) ~ rx, ovarian, dist = "weibull")
newdata <- ovarian[3:5, ]
sum(objfun(x))
x$loglik
objfun(x, newdata = newdata)
}
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