Description Usage Arguments Details Examples

View source: R/DistributionPlot.R

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

1 2 3 4 5 6 7 8 9 10 | ```
PlotDistDensityBeta(
frm,
xvar,
title,
...,
curve_color = "lightgray",
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 |

`curve_color` |
color for empirical density curve |

`beta_color` |
color for matching theoretical beta |

`mean_color` |
color for mean line |

`sd_color` |
color for 1-standard deviation lines (can be NULL) |

Plots the empirical density, the theoretical matching beta, the mean value, and plus/minus one standard deviation from the mean.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
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, ngray/herd_size)
title = "Observed prevalence of gray horses in population"
PlotDistDensityBeta(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
PlotDistDensityBeta(hdata, "rate_gray", title,
sd_color=NULL)
``` |

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