# Plot a Markov model Plots a diagram of a Markov model from the output of corHMM or a custom index matrix ```r plotMKmodel(corhmm.obj, rate.cat = NULL, display = "column", color = c("blue", "red"), arrow.scale = 1, text.scale = 1, vertex.scale = 1) ``` ## Arguments - `corhmm.obj`: an object of class corHMM or matrix. - `rate.cat`: if using a custom matrix then the number of rate categories must be indicated. - `display`: the structure of the plot. one of "column", "square", or "row". - `color`: Either, 1. a vector of 2 colors to create a gradient from low transition rates (first element) to high transition rates (second element), or 2. "col.blind" which will use the color pallete "plasma" from viridis. - `arrow.scale`: determines the size of the arrows for the Markov diagram. - `text.scale`: determines the size of the text for the plotted matrix. - `vertex.scale`: determines the size of the text for the Markov diagram. ## Details Plots Markov models in a ball and stick type diagram next to its corresponding matrix. If plotting a hidden rates model it will produce a compound plot describing how the different rate classes are related to one another. If the input is a `corHMM` result then arrows are colored by relative rate. If the input is a custom matrix arrows are colored by the paramater index. ## Returns Returns a ball and stick diagram of the input model. ## Examples ```r data(primates) phy <- primates[[1]] phy <- multi2di(phy) data <- primates[[2]] # create a legend and rate mat from a multi-character dataset. LegendAndRateMat <- getStateMat4Dat(data) rate.mat <- LegendAndRateMat$rate.mat legend <- LegendAndRateMat$legend # To create a hidden markov model first define your rate classes (state-dependent processes) # R1 will be a manually created SYM model R1 <- equateStateMatPars(rate.mat, c(1:6)) # R2 will only allow transitions between 1 and 2 R2 <- dropStateMatPars(rate.mat, c(3,4)) # R1 and R2 will transtion at equal rates (i.e. the parameter process will be ER) P <- getRateCatMat(2) P <- equateStateMatPars(P, c(1,2)) # combine our state-dependnet processes and parameter process HMM <- getFullMat(list(R1, R2), P) # plot the input plotMKmodel(HMM, rate.cat = 2) # This can now be used in a corHMM model CustomModel <- corHMM(phy = phy, data = data, rate.cat = 2, rate.mat = HMM, node.states = "none") # plot the output plotMKmodel(CustomModel) ``` ## References Boyko, J. D., and J. M. Beaulieu. 2021. Generalized hidden Markov models for phylogenetic comparative datasets. Methods in Ecology and Evolution 12:468-478. ## Author(s) James D. Boyko

plotMKmodel function