plotGradient.ci: Plot evolutionary rates and their confidence regions across a gradient

Description

Takes the output from bootstrap.test for the BM_linear model and plots how evolutionary rates and their confidence regions change across the gradient.

Usage

plotGradient.ci(bootstraps1, bootstraps2=c("FALSE"), Lmin, Lmax, ylim, 
   MLE = FALSE, MLE1, MLE2, xlab="Gradient")

Arguments

bootstraps1

the raw bootstraps output by bootstrap.test

bootstraps2

same as bootstraps1, but for an optional second dataset.

Lmin

minimum gradient value for graph

Lmax

maximum gradient value for graph

ylim

maximum y-axis value

MLE

Logical. If MLE=TRUE, then the maximum likelihood values are plotted. If MLE=FALSE, then the mean bootstrap values are plotted.

MLE1

A list of the maximum likelihood parameter values for dataset 1

MLE2

A list of the maximum likelihood parameter values for dataset 2, if a second dataset provided

xlab

A title for the x-axis.

Value

A plot of the bootstrap 95

Details

Currently, only works for the BM_linear model.

Examples

Run this code

###simulate data
  set.seed(seed = 3)
  TIME = runif(n=200, min = 0, max = 10)
  GRAD = runif(n=200, min = 0, max = 60)
  DATA1 <- sim.sisters(GRAD, TIME, parameters = c(0.1, 0.065), model=c("BM_linear"))

  ###Find the MLE of model parameters
  RESULT <- model.test.sisters(DIST=DATA1[,3], TIME=DATA1[,2], 
     GRAD=DATA1[,1], models=c("BM_linear"))
  intercept <- as.numeric(RESULT[5,1])
  slope <- as.numeric(RESULT[6,1])
  model = c("BM_linear")
  parameters=c(intercept, slope)

  ###Run the bootstrap
  RR <- bootstrap.test(DIST=DATA1[,3], TIME=DATA1[,2],
     GRAD=DATA1[,1], model = "BM_linear", parameters, meserr1=0, 
	 meserr2=0, N = c(100))
  summary <- RR$summary #to show only the summary. 
  bootstraps <- RR$bootstraps #to obtain the bootstraps
  
  ###Plot data
  plotGradient.ci(bootstraps1=bootstraps, 
     bootstraps2= c("FALSE"), Lmin=0, Lmax=60, ylim=c(0,10), 
     MLE=TRUE, MLE1=c(0.1, 0.065), MLE2=c(0,0), xlab="Latitude")#end dontrun