# curve_compare

##### Compare Two Functions and Produces An AUC Score

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

##### Usage

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

##### Arguments

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

- 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().

##### Value

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

##### See Also

##### Examples

```
# NOT RUN {
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]])
curve_compare(intervalsdf[[1]], randomframe[[1]], type = "s")
# }
# NOT RUN {
# }
```

*Documentation reproduced from package concurve, version 2.5.0, License: GPL-3 | file LICENSE*