Hi, Liam,
Thank you for your suggestions. I tried fitMk in phytools and ace in ape.
Below is what I got.
> fit.phytools<-fitMk(tree,bathy2,model="ER",pi="equal")
> fit.phytools
Object of class "fitMk".
Fitted (or set) value of Q:
1shallow 2bathypelagic
1shallow NaN NaN
2bathypelagic NaN NaN
Fitted (or set) value of pi:
1shallow 2bathypelagic
0.5 0.5
Log-likelihood: -1e+50
> fit.ape<-ace(bathy2,tree,type="discrete",model="ER")
Error in matexpo(Q * EL[i]) : NA/NaN/Inf in foreign function call (arg 1)
In addition: Warning message:
In ace(bathy2, tree, type = "discrete", model = "ER") :
model fit suspicious: gradients apparently non-finite
Best wishes, Lei
On Sat, Sep 10, 2016 at 4:22 PM, Liam J. Revell <liam.rev...@umb.edu> wrote:
> Hi Lei.
>
> Looks like an error. The likelihood is actually a very *small* number as
> the log-likelihood is a very *large* negative number. Similarly, the fitted
> transition rates are very small - near zero.
>
> Have you tried to fit the same model using ace in the ape package or fitMk
> in phytools? (These are not totally independent implementations, actually,
> as fitMk borrows some of its code from ace, but nonetheless.) These
> functions should usually result in almost the same fitted model and
> likelihood values as fitDiscrete, but make slightly different assumptions
> about the root state: http://blog.phytools.org/2015/
> 09/the-difference-between-different.html. You can also fit an Mk model
> using diversitree or phangorn. (Look for details on my blog.)
>
> All the best, Liam
>
> Liam J. Revell, Associate Professor of Biology
> University of Massachusetts Boston
> web: http://faculty.umb.edu/liam.revell/
> email: liam.rev...@umb.edu
> blog: http://blog.phytools.org
>
>
> On 9/10/2016 2:00 PM, Lei Yang wrote:
>
>> Dear All,
>>
>> I am learning to use fitDiscrete in geiger recently. Results on several
>> discrete characters look normal except for the following one. Can someone
>> please tell me why the values of log-likelihood, AIC, and AICc are so
>> large? Thanks a lot.
>>
>>
>> ER<-fitDiscrete(tree, aabb, model="ER")
>>>
>>
>> ARD<-fitDiscrete(tree, aabb, model="ARD")
>>>
>>
>> ER
>>>
>>
>> GEIGER-fitted comparative model of discrete data
>>
>> fitted Q matrix:
>>
>> aa bb
>>
>> aa -5.915287 5.915287
>>
>> bb 5.915287 -5.915287
>>
>>
>> model summary:
>>
>> log-likelihood =
>> -99999999999999996973312221251036165947450327545502362648241
>> 750950346848435554075534196338404706251868027512415973882408
>> 182135734368278484639385041047239877871023591066789981811181
>> 813306167128854888448.000000
>>
>> AIC =
>> 199999999999999993946624442502072331894900655091004725296483
>> 501900693696871108151068392676809412503736055024831947764816
>> 364271468736556969278770082094479755742047182133579963622363
>> 626612334257709776896.000000
>>
>> AICc =
>> 199999999999999993946624442502072331894900655091004725296483
>> 501900693696871108151068392676809412503736055024831947764816
>> 364271468736556969278770082094479755742047182133579963622363
>> 626612334257709776896.000000
>>
>> free parameters = 1
>>
>>
>> Convergence diagnostics:
>>
>> optimization iterations = 100
>>
>> failed iterations = 0
>>
>> frequency of best fit = 1.00
>>
>>
>> object summary:
>>
>> 'lik' -- likelihood function
>>
>> 'bnd' -- bounds for likelihood search
>>
>> 'res' -- optimization iteration summary
>>
>> 'opt' -- maximum likelihood parameter estimates
>>
>> ARD
>>>
>>
>> GEIGER-fitted comparative model of discrete data
>>
>> fitted Q matrix:
>>
>> aa bb
>>
>> aa -3.629411e-149 3.629411e-149
>>
>> bb 3.629411e-149 -3.629411e-149
>>
>>
>> model summary:
>>
>> log-likelihood =
>> -99999999999999996973312221251036165947450327545502362648241
>> 750950346848435554075534196338404706251868027512415973882408
>> 182135734368278484639385041047239877871023591066789981811181
>> 813306167128854888448.000000
>>
>> AIC =
>> 199999999999999993946624442502072331894900655091004725296483
>> 501900693696871108151068392676809412503736055024831947764816
>> 364271468736556969278770082094479755742047182133579963622363
>> 626612334257709776896.000000
>>
>> AICc =
>> 199999999999999993946624442502072331894900655091004725296483
>> 501900693696871108151068392676809412503736055024831947764816
>> 364271468736556969278770082094479755742047182133579963622363
>> 626612334257709776896.000000
>>
>> free parameters = 2
>>
>>
>> Convergence diagnostics:
>>
>> optimization iterations = 100
>>
>> failed iterations = 0
>>
>> frequency of best fit = 1.00
>>
>>
>> object summary:
>>
>> 'lik' -- likelihood function
>>
>> 'bnd' -- bounds for likelihood search
>>
>> 'res' -- optimization iteration summary
>>
>> 'opt' -- maximum likelihood parameter estimates
>>
>> ER$opt$aicc
>>>
>>
>> [1] 2e+200
>>
>> ARD$opt$aicc
>>>
>>
>> [1] 2e+200
>>
>>
>>
>> Sincerely, Lei
>>
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
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>> sig-ph...@r-project.org/
>>
>>
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