tectonicr (version 0.4.6)

quick_plot: Plotting Stress Analysis Results

Description

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, a von-Mises QQ plot, and a rose diagram of the quality-weighted frequency distribution of the azimuths.

Usage

quick_plot(azi, distance, prd, unc = NULL, regime, width = 51)

Value

four R base plots

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.

See Also

PoR_shmax(), distance_from_pb(), circular_mean(), circular_dispersion(), confidence_interval_fisher(), norm_chisq(), weighted_rayleigh(), vm_qqplot()

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$azi.PoR,
  distance = d, prd = res$prd, unc = san_andreas$unc,
  regime = san_andreas$regime
)