justicecasey wrote:Is it possible to run a likelihood ratio test in RMark? If so, what is the primary function in R called so I can look up the help files for it. Alternatively, is it discussed in Cooch and White (2012) or any other document that I could look up? Thanks.
data(dipper)
mod.0=mark(dipper)
mod.1=mark(dipper,model.parameters=list(p=list(formula=~time)))
1-pchisq((mod.0$results$lnl-mod.1$results$lnl),df=mod.1$results$npar-mod.0$results$npar)
1 - pchisq(2, df = 1)
[1] 0.1572992
1 - pchisq(2, df = 1) # deviance diff of 2
[1] 0.1572992
1 - pchisq(1, df = 1) # deviance diff of 1
[1] 0.3173105
1 - pchisq(0.5, df = 1) # deviance diff of 0.5
[1] 0.4795001
1 - pchisq(0, df = 1) # deviance diff of 0
[1] 1
jCeradini wrote:Jeff -
I found this post to be very informative with regards to thinking about AIC and so-called uninformative parameters, as described by Burnham and Anderson 2002 (p. 131), and Arnold 2010 JWM. In the discussion on pin-pointing uninformative parameters the authors use phrases like "essentially the same values of the maximized log-likelihood" or "essentially identical..." which I've always struggled to understand.
jCeradini wrote:Great, thanks Evan. I had not seen that (new?) sidebar. That figure helps to solidify what Jeff was explaining. Interesting interaction between the number of parameters added and the p-value at which AIC "retains" a predictor - important aspect of AIC that I don't think many people understand (I certainly didn't fully understand it until about 10 minutes ago ).
Joe
Users browsing this forum: No registered users and 10 guests