# helpers: Different generic functions for classes 'JOC' and 'JOCMV' In PhilipPallmann/jocre: Joint Confidence Regions

## Description

Generic functions for summarising and plotting objects of class `JOC` or `JOCMV`.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```## S3 method for class 'JOC' plot(x, equi=log(c(0.8, 1.25)), axnames=NULL, main=NULL, xlim=log(c(0.77, 1.3)), ylim=log(c(0.77, 1.3)), col="black", convexify=FALSE, ...) ## S3 method for class 'JOC' print(x, digits=max(3, getOption("digits") - 4), ...) ## S3 method for class 'JOC' summary(object, digits=max(3, getOption("digits") - 4), ...) ## S3 method for class 'JOCMV' plot(x, axnames=NULL, main=NULL, xlim=NULL, ylim=NULL, col="black", ...) ## S3 method for class 'JOCMV' print(x, digits=max(3, getOption("digits") - 4), ...) ## S3 method for class 'JOCMV' summary(object, digits=max(3, getOption("digits") - 4), ...) ```

## Arguments

 `x` An output object of class `JOC` or `JOCMV`. `object` An output object of class `JOC` or `JOCMV`. `digits` A numeric value giving the number of significant digits to be printed. `equi` A numeric vector of length 2 specifying the equivalence region (lower and upper equivalence threshold) to be shaded in grey. When set to `NULL` no equivalence region is drawn. Default is `log(c(0.8, 1.25))`. `axnames` A vector of two character strings giving the x and y axis labels. For `plot.JOC`, when set to `NULL` the column names of `dat` are used as axis labels. Default is `NULL`. `main` A character string giving the plot title. Default is `NULL`. `xlim` A numeric vector of length two specifying the plotting range on the x-axis. Default is `log(c(0.77, 1.3))` for `plot.JOC` and `NULL` for `plot.JOCMV`. `ylim` A numeric vector of length two specifying the plotting range on the y-axis. Default is `log(c(0.77, 1.3))` for `plot.JOC` and `NULL` for `plot.JOCMV`. `col` A character string specifying the colour of the plotted region or intervals. `convexify` A logical specifying whether the convex hull around a non-convex region should be plotted instead of the region itself. Ignored unless `method="limacon.fin"` or `method="limacon.asy"` for the `JOC` object. Default is `FALSE`. `...` Further plotting arguments to be passed to methods. Type `?plot` for details.

## Details

`print` and `summary` summarise the estimates and confidence set boundaries of an object of class `JOC` or `JOCMV` that was created with `cset` or `csetMV`, respectively. `plot` displays a (simultaneous) confidence region or intervals when applied to an object of class `JOC` or `JOCMV`.

## Value

An on-screen summary or graphical display.

## Note

Warning: please use with care! Some of the functionality has not yet been thoroughly tested.

## Author(s)

Philip Pallmann ([email protected])

## References

Philip Pallmann & Thomas Jaki (2017) Simultaneous confidence regions and intervals for multivariate bioequivalence. Submitted to Statistics in Medicine.

`cset` and `csetMV` for computing (simultaneous) confidence regions and intervals.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```## Not run: # Example 1: simultaneous 90% confidence region for bivariate data bivar <- mvtnorm::rmvnorm(n=100, mean=rep(0.05, 2), sigma=diag(2) * 0.05) hotelling <- cset(dat=bivar, method="hotelling", alpha=0.1) summary(hotelling) plot(hotelling, main="90% Hotelling Region") # Example 2: simultaneous 90% confidence region for the mean and variance of univariate normal data univar <- rnorm(n=50) moodvar <- csetMV(dat=univar, method="mood", alpha=0.1, scale="var") summary(moodvar) plot(moodvar, main="90% Mood Region") ## End(Not run) ```