real parameter estimates

questions concerning analysis/theory using the R package 'marked'

real parameter estimates

I have been reviewing documentation on marked and something that is not well documented and should be is that the real parameter estimates computed from compute.real and predict.crm are not adjusted for time interval length. This is only relevant to survival which is the only parameter that is scaled on time. Thus the survival estimates are always on the unit interval (eg annual if the unit is 1 year). Hopefully, this was obvious if you looked at beta estimates and computed the real values as a check. If it was not obvious, my sinceres apologies for any problems this may have caused. Below is an example that demonstrates the point. I am going to add an option to compute using time interval value.

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` data(dipper)> dipper.proc=process.data(dipper,model="cjs",begin.time=1)255 capture histories collapsed into 53> dipper.ddl=make.design.data(dipper.proc)> mod.Phit.p=crm(dipper.proc,dipper.ddl,+                 model.parameters=list(Phi=list(formula=~time),p=list(formula=~1)))Computing initial parameter estimatesStarting optimization for 7 parameters... Number of evaluations:  400  -2lnl: 660.2026972Elapsed time in minutes:  0.0045 > mod.Phit.pcrm Model SummaryNpar :  7-2lnL:  659.7301AIC  :  673.7301Beta                    EstimatePhi.(Intercept)  0.514389842Phi.time2       -0.698144440Phi.time3       -0.600934471Phi.time4       -0.006102359Phi.time5       -0.075710457Phi.time6       -0.178061661p.(Intercept)    2.220394714> predict(mod.Phit.p)\$Phi  time occ  estimate1    6   6 0.58329832    5   5 0.60794433    4   4 0.62440494    3   3 0.47837735    2   2 0.45419026    1   1 0.6258350\$p  occ  estimate1   7 0.9020661> > dipper.proc=process.data(dipper,model="cjs",begin.time=1,time.intervals=rep(.5,6))255 capture histories collapsed into 53> dipper.ddl=make.design.data(dipper.proc)> mod.Phit.p=crm(dipper.proc,dipper.ddl,+                 model.parameters=list(Phi=list(formula=~time),p=list(formula=~1)))Computing initial parameter estimatesStarting optimization for 7 parameters... Number of evaluations:  400  -2lnl: 659.7391348Elapsed time in minutes:  0.0038 > mod.Phit.pcrm Model SummaryNpar :  7-2lnL:  659.7301AIC  :  673.7301Beta                    EstimatePhi.(Intercept) -0.440804390Phi.time1.5     -0.906580318Phi.time2       -0.774014992Phi.time2.5     -0.006985002Phi.time3       -0.093134136Phi.time3.5     -0.221426050p.(Intercept)    2.220420652> predict(mod.Phit.p)\$Phi  time occ  estimate1  3.5   6 0.34023882    3   5 0.36959883  2.5   4 0.38988654    2   3 0.22884945  1.5   2 0.20629836    1   1 0.3915493\$p  occ  estimate1   7 0.9020684> 0.3402388^.5[1] 0.5832999`
jlaake

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