Creates a set of plots including the azimuth as a function of the distance to the plate boundary, the Norm Chi-squared as a function of the distance to the plate boundary, the circular distance (and dispersion) a function of the distance to the plate boundary, and a rose diagram of the frequency distribution of the azimuths.

## Arguments

- azi
numeric. Azimuth of \(\sigma_{Hmax}\)

- distance
numeric. Distance to plate boundary

- prd
numeric. the predicted direction of \(\sigma_{Hmax}\)

- unc
numeric. Uncertainty of observed \(\sigma_{Hmax}\), either a numeric vector or a number

- regime
character vector. The stress regime (following the classification of the World Stress Map)

- width
integer. window width (in number of observations) for moving average of the azimuths, circular dispersion, and Norm Chi-square statistics. If

`NULL`

, an optimal width will be estimated.

## Details

Plot 1 shows the transformed azimuths as a function of the distance to the plate boundary. The red line indicates the rolling circular mean, stippled red lines indicate the 95% confidence interval about the mean.

Plot 2 shows the normalized \(\chi^2\) statistics as a function of the distance to the plate boundary. The red line shows the rolling \(\chi^2\) statistic.

Plot 3 shows the circular distance of the transformed azimuths to the predicted azimuth, as a function of the distance to the plate boundary. The red line shows the rolling circular dispersion about the prediction.

Plot 4 give the rose diagram of the transformed azimuths.

## Examples

```
data("nuvel1")
na_pa <- subset(nuvel1, nuvel1$plate.rot == "na")
data("plates")
plate_boundary <- subset(plates, plates$pair == "na-pa")
data("san_andreas")
res <- PoR_shmax(san_andreas, na_pa, "right")
d <- distance_from_pb(san_andreas, na_pa, plate_boundary, tangential = TRUE)
quick_plot(res$azi.PoR, d, res$prd, san_andreas$unc, san_andreas$regime)
```