# qqnorm: Normal quantile plots for compositions and amounts In compositions: Compositional Data Analysis

## Description

The plots allow to check the normal distribution of multiple univaritate marginals by normal quantile-quantile plots. For the different interpretations of amount data a different type of normality is assumed and checked. When an alpha-level is given the marginal displayed in each panel is checked for normality.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```## S3 method for class 'acomp' qqnorm(y,fak=NULL,...,panel=vp.qqnorm,alpha=NULL) ## S3 method for class 'rcomp' qqnorm(y,fak=NULL,...,panel=vp.qqnorm,alpha=NULL) ## S3 method for class 'aplus' qqnorm(y,fak=NULL,...,panel=vp.qqnorm,alpha=NULL) ## S3 method for class 'rplus' qqnorm(y,fak=NULL,...,panel=vp.qqnorm,alpha=NULL) vp.qqnorm(x,y,...,alpha=NULL) ```

## Arguments

 `y` a dataset `fak` a factor to split the dataset, not yet implemented in aplus and rplus `panel` the panel function to be used or a list of multiple panel functions `alpha` the alpha level of a test for normality to be performed for each of the displayed marginals. The levels are adjusted for multiple testing with a Bonferroni-correction (i.e. dividing each of the alpha-level by the number of test performed) `...` further graphical parameters `x` used by pairs only. Internal use

## Details

`qqnorm.rplus` and `qqnorm.rcomp` display qqnorm plots of individual amounts (on the diagonal), of pairwise differences of amounts (above the diagonal) and of pairwise sums of amounts (below the diagonal).
`qqnorm.aplus` displays qqnorm-plots of individual log-amounts (on the diagonal), of pairwise log-ratios of amounts (above the diagonal) and of pairwise sums of log amount (below the diagonal).
`qqnorm.acomp` displays qqnorm-plots of pairwise log-ratios of amounts in all of diagonal panels. Nothing is displayed on the diagonal.
In all cases a joint normality of the original data in the selected framework would imply normality in all displayed marginal distributions (although the reciprocal is in general not true!).
The marginal normality can be checked in each of the plots using a `shapiro.test`, by specifying an alpha level. The alpha level is corrected for multiple testing. Plots displaying a marginal distribution significantly deviating from a normal distribution at that alpha level are marked by a red exclamation mark.
`vp.qqnorm` is internally used as a panel function to make high dimensional plots.

## Author(s)

K.Gerald v.d. Boogaart http://www.stat.boogaart.de

`plot.acomp`, `boxplot.acomp`, `rnorm.acomp`, `rnorm.rcomp`, `rnorm.aplus`, `rnorm.rplus`
 ```1 2 3 4 5``` ```data(SimulatedAmounts) qqnorm(acomp(sa.lognormals),alpha=0.05) qqnorm(rcomp(sa.lognormals),alpha=0.05) qqnorm(aplus(sa.lognormals),alpha=0.05) qqnorm(rplus(sa.lognormals),alpha=0.05) ```