Description Usage Arguments Details Value Examples
Analyze data and report the model estimates and model selection
1 | ouxy(tree = tree, traitset = traitset, tol = 0.1, sims = 100)
|
tree |
An ape: tree object stored in phylo format |
traitset |
a dataframe that contains 3 traits |
tol |
acceptance rate from ABC |
sims |
number of trait replicate |
ouxy
performs data analaysis under Approximate Bayesian Computation(ABC) procedure. The summary statistics for the raw traitsets are first computed by by function sumstat
, and the parameters ranges are computed using the tree and tratisets under function HyperParam
, and sample of prior paramters are drawn from function oubmbmprior
, then the function oubmbmmodel
is applied to simulate traits through post order tree traversal algorithm.
The ABC procedure are then performed using sample of paramters and simulated traitset.
Posterior sample are chosen using acceptance rate sims * tol
. The posterior samples are computed using rejection method abc
to median of the posterior samples are as reported parameter esitmate and Bayes factor is computed using function postpr
accordingly by the ratio of the posterior model probability under each model.
A list of vectors containing a dataframe of model parameter estimate, and a dataframe of Bayes factors between a pair of models
table.output: The posterior median for parameter estiamtes under each model.
s.mnlog: Bayes factor tables comparing a pair of models.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ## using coral dataset (It takes for a whiles)
data(coral)
tree<-coral$tree
traitset<-coral$traitset
sims<-1000
output<-ouxy(tree=tree,traitset=traitset,tol=0.1,sims= sims)
## OUTPUT THE FOLLOWING
## >output$s.mnlog
## $mnlogistic
## $mnlogistic$Prob
## oubmbm oubmcir ououbm ououcir
## 0.03081341 0.01533086 0.40779579 0.54605995
## $mnlogistic$BayesF
## oubmbm oubmcir ououbm ououcir
## oubmbm 1.00000000 2.00989403 0.07556087 0.05642861
## oubmcir 0.49753867 1.00000000 0.03759446 0.02807542
## ououbm 13.23436292 26.59966708 1.00000000 0.74679673
## ououcir 17.72150620 35.61834960 1.33905246 1.00000000
##
## > output$table.out
## alpha.y alpha.x alpha.tau theta.x theta.tau sigma.x
## OUBMBM 4.3064 NA NA NA NA 7.821074
## OUOUBM 4.1240 5.2119 NA -0.5759 NA 10.117253
## OUBMCIR 4.3720 NA 4.0736 NA 1.2326 7.825912
## OUOUCIR 3.1016 4.4269 3.9930 0.0668 1.2702 9.226803
## GLS NA NA NA NA NA NA
##
## tau sigma.tau b0 b1 b2
## OUBMBM 2.2403 NA 0.1678000 0.03850000 0.2874000
## OUOUBM 2.5021 NA 0.1651000 0.03260000 0.3146000
## OUBMCIR NA 1.492548 0.1706000 0.03760000 0.3049000
## OUOUCIR NA 1.516047 0.1661000 0.03480000 0.2549000
## GLS NA NA 0.1682413 0.03931911 0.3564761
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