I just made some
updates
to the new phytools function ratebytree which compares the rate
of continuous character evolution between two or more trees (e.g.,
1,
2).
The method is essentially the same as the censored approach of O'Meara et al.
(2006).
The updates consist of adding the two option arguments: init &
trace.
init, the initial conditions of the optimization, needs to be
supplied as a list with elements corresponding to the different parameters of the
common- and multi-rate models. The names for these elements should be sigc
& ac, for the rate & ancestral states for the common-rate model;
and sigm & sigm for the initial values of the multi-rate
model.
The argument trace is a logical value that tells the function to
trace the progress of the optimization.
So, for instance:
library(phytools)
packageVersion("phytools")
## [1] '0.5.73'
t1
##
## Phylogenetic tree with 26 tips and 25 internal nodes.
##
## Tip labels:
## A, B, C, D, E, F, ...
##
## Rooted; includes branch lengths.
x
## A B C D E F
## -0.1397195 -0.1996443 -0.5827639 -0.3396014 -1.9785818 -2.2452530
## G H I J K L
## 1.5195967 1.6068486 0.5919871 1.6156654 1.2425888 -0.5789243
## M N O P Q R
## -1.1352832 -0.5962740 0.8352019 1.1925811 0.1434599 -0.1649340
## S T U V W X
## -0.2620791 -0.8017158 -0.8558019 -1.2876484 1.1789504 -0.4589235
## Y Z
## 0.7054512 0.4988762
t2
##
## Phylogenetic tree with 40 tips and 39 internal nodes.
##
## Tip labels:
## t4, t8, t9, t1, t25, t26, ...
##
## Rooted; includes branch lengths.
y
## t4 t8 t9 t1 t25 t26
## -0.50044880 -0.33976877 -0.22155547 -1.66246432 0.29014161 0.50776830
## t27 t28 t11 t19 t20 t5
## 0.12476121 0.29866183 1.84948814 -0.63734157 0.70780071 -2.29781879
## t13 t14 t12 t33 t34 t22
## 2.54743880 3.51220356 2.56567363 2.06829938 1.85838926 0.82279334
## t39 t40 t2 t3 t31 t32
## 0.22717955 0.20327362 1.87257199 2.97804036 4.62874247 5.10710664
## t35 t36 t7 t23 t24 t6
## 5.36174612 5.16948164 -0.21639416 -1.44359129 0.05346218 -0.93556170
## t37 t38 t17 t18 t10 t15
## -0.02599676 -0.29860201 -0.35398678 -1.42592744 -0.50920027 3.03128846
## t16 t21 t29 t30
## 2.65445859 -0.41292712 -0.69210806 -0.78368040
t3
##
## Phylogenetic tree with 20 tips and 19 internal nodes.
##
## Tip labels:
## 1, 2, 3, 4, 5, 6, ...
##
## Rooted; includes branch lengths.
z
## 1 2 3 4 5 6
## -0.01050003 -0.48649665 -0.34190675 0.37566749 1.75862664 1.58447168
## 7 8 9 10 11 12
## 1.67743120 0.28316167 0.22031546 1.07939448 -0.69795686 1.63538094
## 13 14 15 16 17 18
## -0.01273937 0.48345033 1.44212231 -0.09246575 0.05411407 1.53395177
## 19 20
## 1.85086405 4.48540981
fit1<-ratebytree(c(t1,t2,t3),list(x,y,z),trace=TRUE,digits=7)
##
## Optimizing multi-rate model....
## sig[1] sig[2] sig[3] logL
## 0.5988996 1.5493864 1.0621323 -109.4408658
## 0.5998996 1.5493864 1.0621323 -109.4417174
## 0.5978996 1.5493864 1.0621323 -109.4400477
## 0.5988996 1.5503864 1.0621323 -109.4411925
## 0.5988996 1.5483864 1.0621323 -109.4405471
## 0.5988996 1.5493864 1.0631323 -109.4413405
## 0.5988996 1.5493864 1.0611323 -109.440399
## 0.5988996 1.5493864 1.0621323 -109.4408677
## 0.5988996 1.5493864 1.0621323 -109.4408677
## 0.5988996 1.5493864 1.0621323 -109.4408661
## 0.5988996 1.5493864 1.0621323 -109.4408661
## 0.5988996 1.5493864 1.0621323 -109.4408666
## 0.5988996 1.5493864 1.0621323 -109.4408666
## 0 1.2266815 0.5913863 -748624418.916421
## 0.001 1.2266815 0.5913863 -7502.1407998
## 0 1.2266815 0.5913863 -748624418.916421
## 0 1.2276815 0.5913863 -748624418.912656
## 0 1.2256815 0.5913863 -748624418.920205
## 0 1.2266815 0.5923863 -748624418.904514
## 0 1.2266815 0.5903863 -748624418.928397
## 0 1.2266815 0.5913863 -748624530.012055
## 0 1.2266815 0.5913863 -748624530.012055
## 0 1.2266815 0.5913863 -748624418.916421
## 0 1.2266815 0.5913863 -748624418.916421
## 0 1.2266815 0.5913863 -748624418.916422
## 0 1.2266815 0.5913863 -748624418.916422
## 0.3996009 1.4419983 0.9054799 -110.4784866
## 0.4006009 1.4419983 0.9054799 -110.4642129
## 0.3986009 1.4419983 0.9054799 -110.4929135
## 0.3996009 1.4429983 0.9054799 -110.4778315
## 0.3996009 1.4409983 0.9054799 -110.4791522
## 0.3996009 1.4419983 0.9064799 -110.4772312
## 0.3996009 1.4419983 0.9044799 -110.479757
## 0.3996009 1.4419983 0.9054799 -110.4784894
## 0.3996009 1.4419983 0.9054799 -110.4784894
## 0.3996009 1.4419983 0.9054799 -110.4784869
## 0.3996009 1.4419983 0.9054799 -110.4784869
## 0.3996009 1.4419983 0.9054799 -110.4784875
## 0.3996009 1.4419983 0.9054799 -110.4784875
## 0.5701688 1.5339054 1.0395494 -109.4190706
## 0.5711688 1.5339054 1.0395494 -109.4188632
## 0.5691688 1.5339054 1.0395494 -109.4193188
## 0.5701688 1.5349054 1.0395494 -109.4192724
## 0.5701688 1.5329054 1.0395494 -109.4188771
## 0.5701688 1.5339054 1.0405494 -109.4193574
## 0.5701688 1.5339054 1.0385494 -109.4187925
## 0.5701688 1.5339054 1.0395494 -109.4190726
## 0.5701688 1.5339054 1.0395494 -109.4190726
## 0.5701688 1.5339054 1.0395494 -109.4190709
## 0.5701688 1.5339054 1.0395494 -109.4190709
## 0.5701688 1.5339054 1.0395494 -109.4190714
## 0.5701688 1.5339054 1.0395494 -109.4190714
## 0.571337 1.5234701 1.0245243 -109.4139814
## 0.572337 1.5234701 1.0245243 -109.4138212
## 0.570337 1.5234701 1.0245243 -109.414182
## 0.571337 1.5244701 1.0245243 -109.4140961
## 0.571337 1.5224701 1.0245243 -109.4138752
## 0.571337 1.5234701 1.0255243 -109.4141336
## 0.571337 1.5234701 1.0235243 -109.4138383
## 0.571337 1.5234701 1.0245243 -109.4139833
## 0.571337 1.5234701 1.0245243 -109.4139833
## 0.571337 1.5234701 1.0245243 -109.4139816
## 0.571337 1.5234701 1.0245243 -109.4139816
## 0.571337 1.5234701 1.0245243 -109.4139822
## 0.571337 1.5234701 1.0245243 -109.4139822
## 0.5761806 1.5104399 1.0080879 -109.4117139
## 0.5771806 1.5104399 1.0080879 -109.4117458
## 0.5751806 1.5104399 1.0080879 -109.4117211
## 0.5761806 1.5114399 1.0080879 -109.4117164
## 0.5761806 1.5094399 1.0080879 -109.4117201
## 0.5761806 1.5104399 1.0090879 -109.4117096
## 0.5761806 1.5104399 1.0070879 -109.4117281
## 0.5761806 1.5104399 1.0080879 -109.4117158
## 0.5761806 1.5104399 1.0080879 -109.4117158
## 0.5761806 1.5104399 1.0080879 -109.4117142
## 0.5761806 1.5104399 1.0080879 -109.4117142
## 0.5761806 1.5104399 1.0080879 -109.4117147
## 0.5761806 1.5104399 1.0080879 -109.4117147
## 0.5758686 1.5107979 1.0089505 -109.4117076
## 0.5768686 1.5107979 1.0089505 -109.4117272
## 0.5748686 1.5107979 1.0089505 -109.4117271
## 0.5758686 1.5117979 1.0089505 -109.4117132
## 0.5758686 1.5097979 1.0089505 -109.4117107
## 0.5758686 1.5107979 1.0099505 -109.4117117
## 0.5758686 1.5107979 1.0079505 -109.4117132
## 0.5758686 1.5107979 1.0089505 -109.4117095
## 0.5758686 1.5107979 1.0089505 -109.4117095
## 0.5758686 1.5107979 1.0089505 -109.4117078
## 0.5758686 1.5107979 1.0089505 -109.4117078
## 0.5758686 1.5107979 1.0089505 -109.4117083
## 0.5758686 1.5107979 1.0089505 -109.4117083
## 0.5758622 1.5107246 1.0089937 -109.4117075
## 0.5768622 1.5107246 1.0089937 -109.4117269
## 0.5748622 1.5107246 1.0089937 -109.4117272
## 0.5758622 1.5117246 1.0089937 -109.4117125
## 0.5758622 1.5097246 1.0089937 -109.4117112
## 0.5758622 1.5107246 1.0099937 -109.411712
## 0.5758622 1.5107246 1.0079937 -109.4117127
## 0.5758622 1.5107246 1.0089937 -109.4117094
## 0.5758622 1.5107246 1.0089937 -109.4117094
## 0.5758622 1.5107246 1.0089937 -109.4117077
## 0.5758622 1.5107246 1.0089937 -109.4117077
## 0.5758622 1.5107246 1.0089937 -109.4117083
## 0.5758622 1.5107246 1.0089937 -109.4117083
##
## Optimizing common-rate model....
## sig logL
## 1.0318602 -112.90808
## 1.0328602 -112.9048916
## 1.0308602 -112.9113149
## 1.0318602 -112.908081
## 1.0318602 -112.908081
## 1.0318602 -112.9080803
## 1.0318602 -112.9080803
## 1.0318602 -112.9080807
## 1.0318602 -112.9080807
## 2.0318602 -119.2505125
## 2.0328602 -119.2601002
## 2.0308602 -119.2409257
## 2.0318602 -119.250513
## 2.0318602 -119.250513
## 2.0318602 -119.2505127
## 2.0318602 -119.2505127
## 2.0318602 -119.2505129
## 2.0318602 -119.2505129
## 1.1400755 -112.800431
## 1.1410755 -112.8013959
## 1.1390755 -112.7994976
## 1.1400755 -112.800432
## 1.1400755 -112.800432
## 1.1400755 -112.8004314
## 1.1400755 -112.8004314
## 1.1400755 -112.8004317
## 1.1400755 -112.8004317
## 1.1153898 -112.7868598
## 1.1163898 -112.7870153
## 1.1143898 -112.7867385
## 1.1153898 -112.7868608
## 1.1153898 -112.7868608
## 1.1153898 -112.7868601
## 1.1153898 -112.7868601
## 1.1153898 -112.7868605
## 1.1153898 -112.7868605
## 1.1111754 -112.7865826
## 1.1121754 -112.7865928
## 1.1101754 -112.7866074
## 1.1111754 -112.7865836
## 1.1111754 -112.7865836
## 1.1111754 -112.786583
## 1.1111754 -112.786583
## 1.1111754 -112.7865834
## 1.1111754 -112.7865834
## 1.1113867 -112.7865819
## 1.1123867 -112.7865993
## 1.1103867 -112.7865992
## 1.1113867 -112.7865829
## 1.1113867 -112.7865829
## 1.1113867 -112.7865822
## 1.1113867 -112.7865822
## 1.1113867 -112.7865826
## 1.1113867 -112.7865826
## 1.1113852 -112.7865819
## 1.1123852 -112.7865993
## 1.1103852 -112.7865993
## 1.1113852 -112.7865829
## 1.1113852 -112.7865829
## 1.1113852 -112.7865822
## 1.1113852 -112.7865822
## 1.1113852 -112.7865826
## 1.1113852 -112.7865826
fit1
## ML common-rate model:
## s^2 a[1] a[2] a[3] k logL
## value 1.1114 -0.2344 0.9677 1.7855 4 -112.7866
##
## ML multi-rate model:
## s^2[1] s^2[2] s^2[3] a[1] a[2] a[3] k logL
## value 0.5759 1.5107 1.009 -0.2344 0.9677 1.7855 6 -109.4117
##
## Likelihood ratio: 6.7497
## P-value (based on X^2): 0.0342
##
## R thinks it has found the ML solution.
Now, how about using different values to commence the optimization:
fit2<-ratebytree(c(t1,t2,t3),list(x,y,z),trace=TRUE,digits=7,
init=list(sigc=2,sigm=rep(2,3)))
##
## Optimizing multi-rate model....
## sig[1] sig[2] sig[3] logL
## 2 2 2 -118.9455945
## 2.001 2 2 -118.9502223
## 1.999 2 2 -118.9409654
## 2 2.001 2 -118.9480425
## 2 1.999 2 -118.9431491
## 2 2 2.001 -118.948072
## 2 2 1.999 -118.9431171
## 2 2 2 -118.9455951
## 2 2 2 -118.9455951
## 2 2 2 -118.9455947
## 2 2 2 -118.9455947
## 2 2 2 -118.9455949
## 2 2 2 -118.9455949
## 1.4226497 1.4226497 1.4226497 -113.9958273
## 1.4236497 1.4226497 1.4226497 -114.0012657
## 1.4216497 1.4226497 1.4226497 -113.9903876
## 1.4226497 1.4236497 1.4226497 -113.9949632
## 1.4226497 1.4216497 1.4226497 -113.9967024
## 1.4226497 1.4226497 1.4236497 -113.997872
## 1.4226497 1.4226497 1.4216497 -113.9937846
## 1.4226497 1.4226497 1.4226497 -113.995828
## 1.4226497 1.4226497 1.4226497 -113.995828
## 1.4226497 1.4226497 1.4226497 -113.9958276
## 1.4226497 1.4226497 1.4226497 -113.9958276
## 1.4226497 1.4226497 1.4226497 -113.9958278
## 1.4226497 1.4226497 1.4226497 -113.9958278
## 0 1.421147 0.7341634 -748624417.333641
## 0.001 1.421147 0.7341634 -7500.5580194
## 0 1.421147 0.7341634 -748624417.333641
## 0 1.422147 0.7341634 -748624417.33276
## 0 1.420147 0.7341634 -748624417.334533
## 0 1.421147 0.7351634 -748624417.328558
## 0 1.421147 0.7331634 -748624417.338757
## 0 1.421147 0.7341634 -748624528.429275
## 0 1.421147 0.7341634 -748624528.429274
## 0 1.421147 0.7341634 -748624417.333641
## 0 1.421147 0.7341634 -748624417.333641
## 0 1.421147 0.7341634 -748624417.333642
## 0 1.421147 0.7341634 -748624417.333642
## 0.948767 1.4221492 1.1933158 -110.963321
## 0.949767 1.4221492 1.1933158 -110.968708
## 0.947767 1.4221492 1.1933158 -110.9579372
## 0.948767 1.4231492 1.1933158 -110.9624514
## 0.948767 1.4211492 1.1933158 -110.9642018
## 0.948767 1.4221492 1.1943158 -110.9646176
## 0.948767 1.4221492 1.1923158 -110.9620293
## 0.948767 1.4221492 1.1933158 -110.9633222
## 0.948767 1.4221492 1.1933158 -110.9633222
## 0.948767 1.4221492 1.1933158 -110.9633213
## 0.948767 1.4221492 1.1933158 -110.9633213
## 0.948767 1.4221492 1.1933158 -110.9633217
## 0.948767 1.4221492 1.1933158 -110.9633217
## 0.4743835 1.4216481 0.9637396 -109.7209006
## 0.4753835 1.4216481 0.9637396 -109.7150794
## 0.4733835 1.4216481 0.9637396 -109.7268043
## 0.4743835 1.4226481 0.9637396 -109.7200254
## 0.4743835 1.4206481 0.9637396 -109.7217869
## 0.4743835 1.4216481 0.9647396 -109.7204189
## 0.4743835 1.4216481 0.9627396 -109.7213941
## 0.4743835 1.4216481 0.9637396 -109.7209029
## 0.4743835 1.4216481 0.9637396 -109.7209029
## 0.4743835 1.4216481 0.9637396 -109.7209009
## 0.4743835 1.4216481 0.9637396 -109.7209009
## 0.4743835 1.4216481 0.9637396 -109.7209014
## 0.4743835 1.4216481 0.9637396 -109.7209014
## 0.7133097 1.4219005 1.0793671 -109.7489584
## 0.7143097 1.4219005 1.0793671 -109.7524779
## 0.7123097 1.4219005 1.0793671 -109.7454546
## 0.7133097 1.4229005 1.0793671 -109.748086
## 0.7133097 1.4209005 1.0793671 -109.7498419
## 0.7133097 1.4219005 1.0803671 -109.7495659
## 0.7133097 1.4219005 1.0783671 -109.7483583
## 0.7133097 1.4219005 1.0793671 -109.7489599
## 0.7133097 1.4219005 1.0793671 -109.7489599
## 0.7133097 1.4219005 1.0793671 -109.7489587
## 0.7133097 1.4219005 1.0793671 -109.7489587
## 0.7133097 1.4219005 1.0793671 -109.7489591
## 0.7133097 1.4219005 1.0793671 -109.7489591
## 0 1.6531789 1.0249276 -748624416.811883
## 0.001 1.6531789 1.0249276 -7500.0362621
## 0 1.6531789 1.0249276 -748624416.811883
## 0 1.6541789 1.0249276 -748624416.812929
## 0 1.6521789 1.0249276 -748624416.810843
## 0 1.6531789 1.0259276 -748624416.812039
## 0 1.6531789 1.0239276 -748624416.811736
## 0 1.6531789 1.0249276 -748624527.907516
## 0 1.6531789 1.0249276 -748624527.907518
## 0 1.6531789 1.0249276 -748624416.811883
## 0 1.6531789 1.0249276 -748624416.811883
## 0 1.6531789 1.0249276 -748624416.811884
## 0 1.6531789 1.0249276 -748624416.811884
## 0.4758741 1.4988849 1.0612462 -109.6770468
## 0.4768741 1.4988849 1.0612462 -109.6713474
## 0.4748741 1.4988849 1.0612462 -109.6828278
## 0.4758741 1.4998849 1.0612462 -109.6769466
## 0.4758741 1.4978849 1.0612462 -109.6771561
## 0.4758741 1.4988849 1.0622462 -109.6775145
## 0.4758741 1.4988849 1.0602462 -109.6765872
## 0.4758741 1.4988849 1.0612462 -109.6770492
## 0.4758741 1.4988849 1.0612462 -109.6770492
## 0.4758741 1.4988849 1.0612462 -109.6770471
## 0.4758741 1.4988849 1.0612462 -109.6770471
## 0.4758741 1.4988849 1.0612462 -109.6770476
## 0.4758741 1.4988849 1.0612462 -109.6770476
## 0.6174096 1.4529944 1.0720481 -109.4758583
## 0.6184096 1.4529944 1.0720481 -109.4772898
## 0.6164096 1.4529944 1.0720481 -109.4744562
## 0.6174096 1.4539944 1.0720481 -109.4753172
## 0.6174096 1.4519944 1.0720481 -109.4764096
## 0.6174096 1.4529944 1.0730481 -109.4764105
## 0.6174096 1.4529944 1.0710481 -109.4753137
## 0.6174096 1.4529944 1.0720481 -109.4758601
## 0.6174096 1.4529944 1.0720481 -109.4758601
## 0.6174096 1.4529944 1.0720481 -109.4758585
## 0.6174096 1.4529944 1.0720481 -109.4758585
## 0.6174096 1.4529944 1.0720481 -109.475859
## 0.6174096 1.4529944 1.0720481 -109.475859
## 0.5490137 1.4881159 1.0371591 -109.4327838
## 0.5500137 1.4881159 1.0371591 -109.4316493
## 0.5480137 1.4881159 1.0371591 -109.4339656
## 0.5490137 1.4891159 1.0371591 -109.4325849
## 0.5490137 1.4871159 1.0371591 -109.432992
## 0.5490137 1.4881159 1.0381591 -109.4330497
## 0.5490137 1.4881159 1.0361591 -109.4325267
## 0.5490137 1.4881159 1.0371591 -109.4327858
## 0.5490137 1.4881159 1.0371591 -109.4327858
## 0.5490137 1.4881159 1.0371591 -109.4327841
## 0.5490137 1.4881159 1.0371591 -109.4327841
## 0.5490137 1.4881159 1.0371591 -109.4327846
## 0.5490137 1.4881159 1.0371591 -109.4327846
## 0.5756508 1.5094148 1.0429111 -109.4171106
## 0.5766508 1.5094148 1.0429111 -109.4171218
## 0.5746508 1.5094148 1.0429111 -109.4171387
## 0.5756508 1.5104148 1.0429111 -109.4171042
## 0.5756508 1.5084148 1.0429111 -109.4171259
## 0.5756508 1.5094148 1.0439111 -109.4174265
## 0.5756508 1.5094148 1.0419111 -109.4168034
## 0.5756508 1.5094148 1.0429111 -109.4171125
## 0.5756508 1.5094148 1.0429111 -109.4171125
## 0.5756508 1.5094148 1.0429111 -109.4171109
## 0.5756508 1.5094148 1.0429111 -109.4171109
## 0.5756508 1.5094148 1.0429111 -109.4171114
## 0.5756508 1.5094148 1.0429111 -109.4171114
## 0.578788 1.4910265 1.0149255 -109.4137608
## 0.579788 1.4910265 1.0149255 -109.4138934
## 0.577788 1.4910265 1.0149255 -109.4136666
## 0.578788 1.4920265 1.0149255 -109.4135888
## 0.578788 1.4900265 1.0149255 -109.413942
## 0.578788 1.4910265 1.0159255 -109.4138229
## 0.578788 1.4910265 1.0139255 -109.4137083
## 0.578788 1.4910265 1.0149255 -109.4137627
## 0.578788 1.4910265 1.0149255 -109.4137627
## 0.578788 1.4910265 1.0149255 -109.4137611
## 0.578788 1.4910265 1.0149255 -109.4137611
## 0.578788 1.4910265 1.0149255 -109.4137616
## 0.578788 1.4910265 1.0149255 -109.4137616
## 0.5759546 1.5111591 1.0083125 -109.4117112
## 0.5769546 1.5111591 1.0083125 -109.4117343
## 0.5749546 1.5111591 1.0083125 -109.4117273
## 0.5759546 1.5121591 1.0083125 -109.41172
## 0.5759546 1.5101591 1.0083125 -109.4117112
## 0.5759546 1.5111591 1.0093125 -109.4117091
## 0.5759546 1.5111591 1.0073125 -109.4117232
## 0.5759546 1.5111591 1.0083125 -109.4117131
## 0.5759546 1.5111591 1.0083125 -109.4117131
## 0.5759546 1.5111591 1.0083125 -109.4117115
## 0.5759546 1.5111591 1.0083125 -109.4117115
## 0.5759546 1.5111591 1.0083125 -109.411712
## 0.5759546 1.5111591 1.0083125 -109.411712
## 0.5758751 1.5106832 1.0090684 -109.4117074
## 0.5768751 1.5106832 1.0090684 -109.4117274
## 0.5748751 1.5106832 1.0090684 -109.4117267
## 0.5758751 1.5116832 1.0090684 -109.4117121
## 0.5758751 1.5096832 1.0090684 -109.4117116
## 0.5758751 1.5106832 1.0100684 -109.4117128
## 0.5758751 1.5106832 1.0080684 -109.4117119
## 0.5758751 1.5106832 1.0090684 -109.4117094
## 0.5758751 1.5106832 1.0090684 -109.4117094
## 0.5758751 1.5106832 1.0090684 -109.4117077
## 0.5758751 1.5106832 1.0090684 -109.4117077
## 0.5758751 1.5106832 1.0090684 -109.4117082
## 0.5758751 1.5106832 1.0090684 -109.4117082
## 0.5758675 1.5106518 1.0090267 -109.4117074
## 0.5768675 1.5106518 1.0090267 -109.4117271
## 0.5748675 1.5106518 1.0090267 -109.411727
## 0.5758675 1.5116518 1.0090267 -109.4117118
## 0.5758675 1.5096518 1.0090267 -109.4117118
## 0.5758675 1.5106518 1.0100267 -109.4117123
## 0.5758675 1.5106518 1.0080267 -109.4117123
## 0.5758675 1.5106518 1.0090267 -109.4117094
## 0.5758675 1.5106518 1.0090267 -109.4117094
## 0.5758675 1.5106518 1.0090267 -109.4117077
## 0.5758675 1.5106518 1.0090267 -109.4117077
## 0.5758675 1.5106518 1.0090267 -109.4117082
## 0.5758675 1.5106518 1.0090267 -109.4117082
##
## Optimizing common-rate model....
## sig logL
## 2 -118.9455945
## 2.001 -118.9551477
## 1.999 -118.9360425
## 2 -118.9455951
## 2 -118.9455951
## 2 -118.9455947
## 2 -118.9455947
## 2 -118.9455949
## 2 -118.9455949
## 1 -113.0350348
## 1.001 -113.0302715
## 0.999 -113.0398507
## 1 -113.0350359
## 1 -113.0350359
## 1 -113.0350352
## 1 -113.0350352
## 1 -113.0350356
## 1 -113.0350356
## 1.3339499 -113.4612924
## 1.3349499 -113.4666788
## 1.3329499 -113.4559222
## 1.3339499 -113.4612933
## 1.3339499 -113.4612933
## 1.3339499 -113.4612928
## 1.3339499 -113.4612928
## 1.3339499 -113.4612931
## 1.3339499 -113.4612931
## 1.1138276 -112.7866855
## 1.1148276 -112.7867874
## 1.1128276 -112.7866181
## 1.1138276 -112.7866865
## 1.1138276 -112.7866865
## 1.1138276 -112.7866858
## 1.1138276 -112.7866858
## 1.1138276 -112.7866862
## 1.1138276 -112.7866862
## 1.1118507 -112.7865857
## 1.1128507 -112.7866192
## 1.1108507 -112.7865868
## 1.1118507 -112.7865867
## 1.1118507 -112.7865867
## 1.1118507 -112.786586
## 1.1118507 -112.786586
## 1.1118507 -112.7865864
## 1.1118507 -112.7865864
## 1.1113832 -112.7865819
## 1.1123832 -112.7865992
## 1.1103832 -112.7865994
## 1.1113832 -112.7865829
## 1.1113832 -112.7865829
## 1.1113832 -112.7865822
## 1.1113832 -112.7865822
## 1.1113832 -112.7865826
## 1.1113832 -112.7865826
## 1.1113852 -112.7865819
## 1.1123852 -112.7865993
## 1.1103852 -112.7865993
## 1.1113852 -112.7865829
## 1.1113852 -112.7865829
## 1.1113852 -112.7865822
## 1.1113852 -112.7865822
## 1.1113852 -112.7865826
## 1.1113852 -112.7865826
fit2
## ML common-rate model:
## s^2 a[1] a[2] a[3] k logL
## value 1.1114 -0.2344 0.9677 1.7855 4 -112.7866
##
## ML multi-rate model:
## s^2[1] s^2[2] s^2[3] a[1] a[2] a[3] k logL
## value 0.5759 1.5107 1.009 -0.2344 0.9677 1.7855 6 -109.4117
##
## Likelihood ratio: 6.7497
## P-value (based on X^2): 0.0342
##
## R thinks it has found the ML solution.
Neat.
Here's a visualization of the evolution of our tree different trees as I showed last time:
ylim<-range(c(x,y,z))
par(mfrow=c(1,3))
phenogram(t1,x,ylim=ylim,spread.cost=c(1,0),ftype="i")
phenogram(t2,y,ylim=ylim,spread.cost=c(1,0),ftype="i")
## Optimizing the positions of the tip labels...
phenogram(t3,z,ylim=ylim,spread.cost=c(1,0),ftype="i")
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