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We have created the equivalent MARK model and saved the MARK project here. You will see in the results browser the single model Phi(g*t)p(t) and the identical deviance value (the AIC values differ because of different parameter counting).If you right click and select "output in Notepad" you will see the saved log likelihood, deviance and c-hat statistics that RMark produced.The above results suggest a need for a slight overdispersion correction (c-hat =1.06).
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A similar procedure is enabled in RMark as described below.
An older bootstrap simulation tool is also available for CJS and a few other structures (e.g., the Seber parameterization of dead recovery models).
I will illustrate each of a series of approaches by way of the Dipper example, an "ideal example" in that quite a number of approaches for GOF will work for the CJS model structure, but the example also can be used to illustrate some more "non standard" approaches. We can read c-hat from the output or calculate it from the model object asand then read this into MARK and run the Phi(g*t) p(g*t) model.
It is also possible to directly export models already run in RMark and import them into MARK.
If we are using deviance as our discrepancy measure we can calculate the mean of the simulated deviances.