Compares the p-value/s-value, and likelihood functions and computes an AUC number.

curve_compare(data1, data2, type = "c", plot = TRUE, ...)

data1 | The first dataframe produced by one of the interval functions in which the intervals are stored. |
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data2 | The second dataframe produced by one of the interval functions in which the intervals are stored. |

type | Choose whether to plot a "consonance" function, a "surprisal" function or "likelihood". The default option is set to "c". The type must be set in quotes, for example curve_compare (type = "s") or curve_compare(type = "c"). Other options include "pd" for the consonance distribution function, and "cd" for the consonance density function, "l1" for relative likelihood, "l2" for log-likelihood, "l3" for likelihood and "d" for deviance function. |

plot | by default it is set to TRUE and will use the plot_compare() function to plot the two functions. |

... | Can be used to pass further arguments to plot_compare(). |

Computes an AUC score and returns a plot that graphs two functions.

if (FALSE) { library(concurve) GroupA <- rnorm(50) GroupB <- rnorm(50) RandomData <- data.frame(GroupA, GroupB) intervalsdf <- curve_mean(GroupA, GroupB, data = RandomData) GroupA2 <- rnorm(50) GroupB2 <- rnorm(50) RandomData2 <- data.frame(GroupA2, GroupB2) model <- lm(GroupA2 ~ GroupB2, data = RandomData2) randomframe <- curve_gen(model, "GroupB2") curve_compare(intervalsdf[[1]], randomframe[[1]]) }