Hi Lei.
It looks like you have something peculiar going on with your tree or
data. If you share it then I'd be happy to try to investigate further;
however without the data it will be hard to figure this one out.
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 8:59 PM, Lei Yang wrote:
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
<mailto: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
<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/
<http://faculty.umb.edu/liam.revell/>
email: liam.rev...@umb.edu <mailto: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 =
-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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