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 =
-99999999999999996973312221251036165947450327545502362648241750950346848435554075534196338404706251868027512415973882408182135734368278484639385041047239877871023591066789981811181813306167128854888448.000000
AIC =
199999999999999993946624442502072331894900655091004725296483501900693696871108151068392676809412503736055024831947764816364271468736556969278770082094479755742047182133579963622363626612334257709776896.000000
AICc =
199999999999999993946624442502072331894900655091004725296483501900693696871108151068392676809412503736055024831947764816364271468736556969278770082094479755742047182133579963622363626612334257709776896.000000
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 =
-99999999999999996973312221251036165947450327545502362648241750950346848435554075534196338404706251868027512415973882408182135734368278484639385041047239877871023591066789981811181813306167128854888448.000000
AIC =
199999999999999993946624442502072331894900655091004725296483501900693696871108151068392676809412503736055024831947764816364271468736556969278770082094479755742047182133579963622363626612334257709776896.000000
AICc =
199999999999999993946624442502072331894900655091004725296483501900693696871108151068392676809412503736055024831947764816364271468736556969278770082094479755742047182133579963622363626612334257709776896.000000
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
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