knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 6 )
library(BCEA) library(dplyr) library(reshape2) library(ggplot2) library(purrr)
The intention of this vignette is to show how to plot different styles of cost-effectiveness acceptability curves using the BCEA package.
To calculate these in BCEA we use the bcea()
function.
data("Vaccine") he <- bcea(eff, cost)
The plot defaults to base R plotting. Type of plot can be set explicitly using the graph
argument.
ceplane.plot(he, graph = "base") ceplane.plot(he, graph = "ggplot2") # ceac.plot(he, graph = "plotly")
Other plotting arguments can be specified such as title, line colours and theme.
ceplane.plot(he, graph = "ggplot2", title = "my title", line = list(color = "green", size = 3), point = list(color = "blue", shape = 10, size = 5), icer = list(color = "orange", size = 5), area = list(fill = "grey"), theme = theme_linedraw())
If you only what the mean point then you can suppress the sample points by passing size NA
.
ceplane.plot(he, graph = "ggplot2", point = list(size = NA), icer = list(size = 5))
This situation is when there are more than two interventions to consider.
data("Smoking") he <- bcea(eff, cost, ref = 4) # str(he)
ceplane.plot(he) ceplane.plot(he, graph = "ggplot2")
ceplane.plot(he, graph = "ggplot2", title = "my title", line = list(color = "red", size = 1), point = list(color = c("plum", "tomato", "springgreen"), shape = 3:5, size = 2), icer = list(color = c("red", "orange", "black"), size = 5))
Reposition legend.
ceplane.plot(he, pos = FALSE) # bottom right ceplane.plot(he, pos = c(0, 0)) ceplane.plot(he, pos = c(0, 1)) ceplane.plot(he, pos = c(1, 0)) ceplane.plot(he, pos = c(1, 1))
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