library(bbmle)

set.seed(101)
z = rpois(100,lambda=5)

m1 = mle2(z~dpois(lambda=L),start=list(L=4),data=data.frame(z))
q1 <- qAICc(m1,nobs=100,dispersion=1.2)
q1m <- qAICc(m1,m1,nobs=100,dispersion=1.2)
## GH #22: single- and multi-object qAICc() calls disagreed because the
## single-object branch omitted the "+1 for scale parameter" df adjustment
## that the multi-object branch applied
## IGNORE_RDIFF_BEGIN
stopifnot(isTRUE(all.equal(unname(q1), q1m$AICc[1])),
          isTRUE(all.equal(q1m$AICc[1], q1m$AICc[2])),
          isTRUE(all.equal(unname(q1), 374.221382320186)))
## IGNORE_RDIFF_END
i1 <- ICtab(m1,type="qAICc",dispersion=1.2,nobs=100, base=TRUE)

m2 = glm(z~1,family=poisson)
q2 <- qAICc(m2,nobs=100,dispersion=1.2)

## test that dAIC ignores
m3 <- glm(z~1,family=quasipoisson)
aa <- AICtab(m1,m2,m3,weights=TRUE)
stopifnot(any(!is.na(aa$dAIC)),
          any(!is.na(aa$weight)))

set.seed(101)
x <- rnorm(100)
dd <- data.frame(y=rnorm(100,2+3*x,sd=1),x)
m4A <- lm(y~x,dd)
m4B <- mle2(y~dnorm(mean=a+b*x,sd=exp(logsd)),
           data=dd,
           start=list(a=1,b=1,logsd=0))
## cosmetic differences only
stopifnot(all.equal(AIC(m4A,m4B)[,"AIC"],
                    AIC(m4B,m4A)[,"AIC"]))