bgmyc_tbl() processes output from bgmyc.singlephy into an
object of class tbl_df.
Source
Reid N.M., Carstens B.C. 2012. Phylogenetic estimation error can decrease the accuracy of species delimitation: a Bayesian implementation of the general mixed Yule-coalescent model. BMC Evolutionary Biology 12 (196).
Arguments
- bgmyc_res
Output from bgmyc.singlephy.
- ppcutoff
Posterior probability threshold for clustering samples into species partitions. See bgmyc.point for details. Default to 0.05.
- delimname
Character. String to rename the delimitation method in the table. Default to 'bgmyc'.
Value
an object of class tbl_df.
Details
bGMYC package uses spec.probmat to create a
matrix of probability of conspecificity and bgmyc.point
to split samples into a list which individuals
meets the threshold specified by ppcutoff. bgmyc_tbl() wraps up these
two functions into a single one and turns these inputs into a tibble which matches
the output from gmyc_tbl and locmin_tbl.
Examples
# \donttest{
# run bGMYC
bgmyc_res <- bGMYC::bgmyc.singlephy(ape::as.phylo(geophagus_beast),
mcmc = 11000,
burnin = 1000,
thinning = 100,
t1 = 2,
t2 = ape::Ntip(geophagus_beast),
start = c(1, 0.5, 50)
)
#> You are running bGMYC on a single phylogenetic tree.
#> This tree contains 137 tips.
#> The Yule process rate change parameter has a uniform prior ranging from 0 to 2 .
#> The coalescent process rate change parameter has a uniform prior ranging from 0 to 2 .
#> The threshold parameter, which is equal to the number of species, has a uniform prior ranging from 2 to 137 . The upper bound of this prior should not be more than the number of tips in your trees.
#> The MCMC will start with the Yule parameter set to 1 .
#> The MCMC will start with the coalescent parameter set to 0.5 .
#> The MCMC will start with the threshold parameter set to 50 . If this number is greater than the number of tips in your tree, an error will result.
#> Given your settings for mcmc, burnin and thinning, your analysis will result in 100 samples being retained.
#> 10 %
#> 20 %
#> 30 %
#> 40 %
#> 50 %
#> 60 %
#> 80 %
#> 90 %
#> 100 %
#> acceptance rates
#> py pc th
#> 0.5518182 0.5462727 0.2234545
# create a tibble
bgmyc_df <- bgmyc_tbl(bgmyc_res, ppcutoff = 0.05)
# check
bgmyc_df
#> # A tibble: 137 × 2
#> labels bgmyc
#> <chr> <int>
#> 1 GU701784.1 1
#> 2 GU701785.1 1
#> 3 JN988869.1 1
#> 4 MH780911.1 1
#> 5 OR732927.1 1
#> 6 OR732928.1 1
#> 7 MZ050845.1 2
#> 8 MZ051032.1 2
#> 9 MZ051706.1 2
#> 10 MZ051794.1 2
#> # ℹ 127 more rows
# }