`R/DistributionPlot.R`

`PlotDistHistBeta.Rd`

Compares empirical rate data to a beta distribution with the same mean and standard deviation.

PlotDistHistBeta( frm, xvar, title, ..., bins = 30, hist_color = "darkgray", beta_color = "blue", mean_color = "blue", sd_color = "darkgray" )

frm | data frame to get values from |
---|---|

xvar | name of the independent (input or model) column in frame |

title | title to place on plot |

... | force later arguments to bind by name |

bins | passed to geom_histogram(). Default: 30 |

hist_color | color of empirical histogram |

beta_color | color of matching theoretical beta |

mean_color | color of mean line |

sd_color | color of 1-standard devation lines (can be NULL) |

ggplot2 plot

Plots the histogram of the empirical distribution and the density of the matching beta distribution. Also plots the mean and plus/minus one standard deviation.

The number of bins for the histogram defaults to 30. The binwidth can also be passed in instead of the number of bins.

set.seed(52523) N = 100 pgray = 0.1 # rate of gray horses in the population herd_size = round(runif(N, min=25, 50)) ngray = rbinom(N, herd_size, pgray) hdata = data.frame(n_gray=ngray, herd_size=herd_size) # observed rate of gray horses in each herd hdata$rate_gray = with(hdata, n_gray/herd_size) title = "Observed prevalence of gray horses in population" PlotDistHistBeta(hdata, "rate_gray", title) + ggplot2::geom_vline(xintercept = pgray, linetype=4, color="maroon") + ggplot2::annotate("text", x=pgray+0.01, y=0.01, hjust="left", label = paste("True prevalence =", pgray))# no sd lines PlotDistHistBeta(hdata, "rate_gray", title, sd_color=NULL)