logLik method for brownie.lite

I just pushed a small update to phytools giving the popular brownie.lite function a logLik S3 method.

brownie.lite fits a multi-rate Brownian motion model originally developed by Brian O'Meara et al. (2006).

Here, I'll show how it can be used in combination with the generic AIC method to do model selection or to compute model weights:

library(phytools)
tree

## 
## Phylogenetic tree with 26 tips and 25 internal nodes.
## 
## Tip labels:
##  A, B, C, D, E, F, ...
## 
## The tree includes a mapped, 2-state discrete character with states:
##  a, b
## 
## Rooted; includes branch lengths.

x

##            A            B            C            D            E 
##  -4.42905986  -4.33868707  -5.30046141  -3.05157557  -3.82136084 
##            F            G            H            I            J 
##  -3.84278970  -4.28031068  -4.79455901  -5.81169561  -6.34045086 
##            K            L            M            N            O 
##  -1.65886570  -9.33620130 -10.28889806   0.41905850   0.62457524 
##            P            Q            R            S            T 
##   1.69227697   1.14144175  -0.02283264  -1.98316222  -3.61491689 
##            U            V            W            X            Y 
##   1.71643871   2.75486352   0.13485761   0.21096964   0.47606198 
##            Z 
##  -0.38421995

cols<-setNames(c("blue","red"),c("a","b"))
phenogram(tree,x,colors=cols)

##            1            2            3            4            5 
##  -5.01570904  -4.67036911  -5.70806984  -2.94258054  -3.63500326 
##            6            7            8            9           10 
##  -3.98014777  -4.32528265  -5.36044268  -6.05326441  -6.58088057 
##           11           12           13           14           15 
##  -1.76580067  -9.37186885 -10.28889806   0.36848651   1.05920932 
##           16           17           18           19           20 
##   1.74996436   1.40415384  -0.66609199  -2.11198563  -3.28891866 
##           21           22           23           24           25 
##   2.09536923   2.75486352  -0.32092830   0.02392802   0.71404775 
##           26 
##  -1.01180397

## fit two-rate model using brownie.lite
fit<-brownie.lite(tree,x)
fit

## ML single-rate model:
##  s^2 se  a   k   logL
## value    2.2226  0.6164  -1.4325 2   -45.13  
## 
## ML multi-rate model:
##  s^2(a)  se(a)   s^2(b)  se(b)   a   k   logL    
## value    1.0376  0.4312  3.6835  1.5578  -0.6666 3   -43.2547
## 
## P-value (based on X^2): 0.0528 
## 
## R thinks it has found the ML solution.

liks<-logLik(fit)
liks

## single-rate  multi-rate 
##   -45.12998   -43.25468 
## attr(,"df")
## [1] 2 3

AIC<-setNames(AIC(fit),names(liks))
AIC

## single-rate  multi-rate 
##    94.25996    92.50935

aic.w(AIC)

## single-rate  multi-rate 
##    0.294152    0.705848

That could be helpful.

The data were simulated as follows:

set.seed(1)
tree<-pbtree(n=26,tip.label=LETTERS)
tree<-paintSubTree(tree,29,"b","a",stem=0.5)
x<-sim.rates(tree,setNames(c(1,2.5),letters[1:2]))