https://github.com/cran/bbmle
Tip revision: 0877514d5b6d663f593fdd6d43310db0c48a650e authored by Ben Bolker on 14 September 2011, 15:14:32 UTC
version 1.0.3
version 1.0.3
Tip revision: 0877514
glmcomp.R
library(bbmle)
x <- 0:10
y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
d <- data.frame(x,y)
LL <- function(ymax=15, xhalf=6)
-sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE))
mfit0 <- mle2(y~dpois(lambda=exp(interc)),
start=list(interc=log(mean(y))),data=d)
mfit1 <- mle2(y~dpois(lambda=exp(loglambda)),
start=list(loglambda=log(mean(y))),data=d)
gfit0 <- glm(y~1,family=poisson)
coef(mfit0)-coef(gfit0)
logLik(mfit0)-logLik(gfit0)
predict(gfit0,type="response")
predict(mfit0) ## why only one value??
## FIXME: residuals are backwards
residuals(mfit0)
residuals(gfit0,type="response")