In MingweiWilliamTang/BMIthreshCount: Counting label transitions in phylogenetic stochastic mapping

This is a R package doing statistical inference gaining counting the number of gaining traits during evolution. The model is based on phlyogenetic stochastic mapping and using Brownian motion interval crossing model for binary trait transition. With the help of BMI_thresh, we can do fellowing things

• Calculating prior and posterior probabilities for interval crossing on a single line
• Calculating prior and posterior probability for crossing numbers on a given phylogenetic tree
• Run Calibrated Brownian motion method (CBM) to get the bayes factors and results on test the transition numbers

library(BMIthreshCount)

Brownian motion interval crossing model

set.seed(223)
# simulate a Brown motion path and plot
path <- OUbridge(1,-1,1,1000,0,0,1,T)
# counting how many upcrossing and downcrossing and find the crossing points
crossing(path,0,0.2,0,1)

Also, given a starting point x0 and an ending point xt, the posterior probablity of crossing number $Pr(N=n,L_0=l|X_0=x_0,X_t=x_t)$ can be calculated

crossing_brownbridge(1,-1,1,1,0.1,T,F)

Calculate prior and posterior probility for crossing number on phylogenetic tree

set.seed(518)
library(geiger)
phytree <- rtree(10)
plot(phytree)

Figures

The figure sizes have been customised so that you can easily put two images side-by-side.

plot(1:10)
plot(10:1)

You can enable figure captions by fig_caption: yes in YAML:

output:
  rmarkdown::html_vignette:
    fig_caption: yes

Then you can use the chunk option fig.cap = "Your figure caption." in knitr.

More Examples

You can write math expressions, e.g. $Y = X\beta + \epsilon$, footnotes^[A footnote here.], and tables, e.g. using knitr::kable().

knitr::kable(head(mtcars, 10))

Also a quote using >:

"He who gives up [code] safety for [code] speed deserves neither." (via)

MingweiWilliamTang/BMIthreshCount documentation built on May 7, 2019, 4:57 p.m.