--- title: "Circular statistics" author: "Tobias Stephan" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Circular statistics} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} editor_options: markdown: wrap: 72 --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) set.seed(20250411) ``` This vignette teaches you how to retrieve statistical parameters of orientation data, for example the mean direction of stress datasets. ```{r setup, echo=TRUE, message=FALSE} library(tectonicr) library(ggplot2) # load ggplot library ``` ## Orientation data types Circular orientation data are two-dimensional orientation vectors that are either **directional** or **axial**. - **Directional** data are $2\pi$-periodical, the angle $\alpha$ is non-symmetrical on a circle. The modulus of the angle is $2\pi$ (360°): α = α mod $2\pi$. Directional data usually involve processes where objects or particles were transported from one known place to another. Examples are wind direction, bird vanishing angles, plate motion, and fault slip directions. - **Axial** data are $\pi$-periodical, i.e. each direction is considered as equivalent to the opposite direction, so that the angles $\alpha$ and $\alpha + 180^\circ$ are equivalent. The modulus of the angle is $\pi$ (180°): α = α mod $\pi$. Data usually involves transport of objects where the origin is unknown or doesn't matter as the transport could be extended infinitely. Examples are glacial striae, mineral alignments (long-axis of grain or crystal shapes), and principal stress/strain axes (e.g. shortening or compression direction, including the angle of the maximum horizontal stress $\sigma_\text{Hmax}$). > In (almost) every function in the **tectonicr** library, the `axial` > argument specifies whether your angles `x` are axial (`TRUE`) or > directional (`FALSE`). Because this package is mainly designed for > analyzing stress orientations, the default is `axial=TRUE`. ```{r directional_vs_axial, echo=TRUE} angles <- rvm(10, mean = 35, kappa = 20) par(mfrow = c(1, 2), xpd = NA) circular_plot(main = "Directional") rose_line(angles, axial = FALSE, col = "#B63679FF") circular_plot(main = "Axial") rose_line(angles, axial = TRUE, col = "#B63679FF") ``` ## Mean direction In case of axial data, the calculation of the mean of, say, of 35$^{\circ}$ and 355$^{\circ}$ should be 15 instead of 195$^{\circ}$. **tectonicr** provides the circular mean (`circular_mean()`) and the quasi-median (`circular_median()`) as metrics to describe average direction: ```{r mean, echo=TRUE} data("san_andreas") circular_mean(san_andreas$azi) circular_median(san_andreas$azi) ``` > Again: if your angles are directional, you need to specify the `axial` > argument. Note the different results: ```{r mean_directional, echo=TRUE} circular_mean(san_andreas$azi, axial = FALSE) circular_median(san_andreas$azi, axial = FALSE) ``` ## Quality weighted mean direction Because the stress data is heteroscedastic, the data with less precise direction should have less impact on the final mean direction The weighted mean or quasi-median uses the reported measurements linear weighted by the inverse of the uncertainties: ```{r weighted, echo=TRUE} w <- weighting(san_andreas$unc) circular_mean(san_andreas$azi, w) circular_median(san_andreas$azi, w) ``` The spread of directional data can be expressed by the standard deviation (for the mean) or the quasi-interquartile range (for the median): ```{r weighted_spread, echo=TRUE} circular_sd(san_andreas$azi, w) # standard deviation circular_IQR(san_andreas$azi, w) # interquartile range ``` ## Statistics in the Pole of Rotation (PoR) reference frame **NOTE:** Because the $\sigma_{SHmax}$ orientations are subjected to angular distortions in the geographical coordinate system, it is recommended to express statistical parameters using the transformed orientations of the PoR reference frame. ```{r por, echo=TRUE} data("cpm_models") por <- cpm_models[["NNR-MORVEL56"]] |> equivalent_rotation("na", "pa") san_andreas.por <- PoR_shmax(san_andreas, por, type = "right") ``` ```{r por_stats, echo=TRUE} circular_mean(san_andreas.por$azi.PoR, w) circular_sd(san_andreas.por$azi.PoR, w) circular_median(san_andreas.por$azi.PoR, w) circular_IQR(san_andreas.por$azi.PoR, w) ``` The collected summary statistics can be quickly obtained by `circular_summary()`: ```{r summary_stats, echo=TRUE} circular_summary(san_andreas.por$azi.PoR, w, mode = TRUE) ``` The summary statistics additionally include the circular quasi-quantiles, the variance, the skewness, the kurtosis, the mode, the 95% confidence angle, and the mean resultant length (R). ## Rose diagram **tectonicr** provides a rose diagram, i.e. histogram for angular data. ```{r rose1, echo=TRUE} rose(san_andreas$azi, weights = w, main = "North pole", dots = TRUE, stack = TRUE, dot_cex = 0.5, dot_pch = 21 ) # add the density curve plot_density(san_andreas$azi, kappa = 20, col = "#51127CFF", scale = 1.1, shrink = 2, xpd = NA) ``` The diagram shows the uncertainty-weighted frequencies (equal area rose fans), the von Mises density distribution (blue curve), and the circular mean (red line) incl. its 95% confidence interval (transparent red). Showing the distribution of the transformed data gives the better representation of the angle distribution as there is no angle distortion due to the arbitrarily chosen geographic coordinate system. ```{r rose2, echo=TRUE} rose(san_andreas.por$azi, weights = w, main = "PoR", dots = TRUE, stack = TRUE, dot_cex = 0.5, dot_pch = 21 ) plot_density(san_andreas.por$azi, kappa = 20, col = "#51127CFF", scale = 1.1, shrink = 2, xpd = NA) # show the predicted direction rose_line(135, radius = 1.1, col = "#FB8861FF") ``` The green line shows the predicted direction. ## QQ Plot The (linearised) circular QQ-Plot (`circular_qqplot()`) can be used to visually assess whether our stress sample is drawn from an uniform distribution or has a preferred orientation. ```{r qqplot, echo=TRUE} circular_qqplot(san_andreas.por$azi.PoR) ``` Our data clearly deviates from the diagonal line, indicating the data is not randomly distributed and has a strong preferred orientation around the 50% quantile. ## Statistical tests ### Test for random distribution Uniformly distributed orientation can be described by the *von Mises distribution*[^1]. If the directions are distributed randomly can be tested with the **Rayleigh Test**: [^1]: Mardia, K. V., and Jupp, P. E. (Eds.). (1999). "Directional Statistics" Hoboken, NJ, USA: John Wiley & Sons, Inc. doi: 10.1002/9780470316979. ```{r random, echo=TRUE} rayleigh_test(san_andreas.por$azi.PoR) ``` Here, the test rejects the Null Hypothesis (`statistic > p.value`). Thus the $\sigma_{SHmax}$ directions have a preferred orientation. Alternative statistical tests for circular uniformity are `kuiper_test()` and `watson_test()`. Read `help()` for more details... ## Test for goodness-of-fit ### Confidence intervals Assuming a von Mises Distribution (circular normal distribution) of the orientation data, a $100(1-\alpha)\%$ **confidence interval**[^2] can be calculated: [^2]: Mardia, K. V., and Jupp, P. E. (Eds.). (1999). "Directional Statistics" Hoboken, NJ, USA: John Wiley & Sons, Inc. doi: 10.1002/9780470316979. ```{r confidence, echo=TRUE} confidence_interval(san_andreas.por$azi.PoR, conf.level = 0.95, w = w) ``` The prediction for the $\sigma_{SHmax}$ orientation is $135^{\circ}$. Since the prediction lies within the confidence interval, it can be concluded with 95% confidence that the orientations follow the predicted trend of $\sigma_{SHmax}$. ### Circular dispersion The (weighted) **circular dispersion** of the orientation angles around the prediction is another way of assessing the significance of a normal distribution around a specified direction. The measure is based on the circular distance[^3] defined as $$d = 1 - \cos{\left[ k(\theta - \mu)\right]}$$ where $\theta$ are the observed angles (here $\sigma_{Hmax}$), $\mu$ is the theoretical angles, and $k=1$ for directional data and $k=2$ for directional data. The (weighted) dispersion[^4] is [^3]: Mardia, K. V., and Jupp, P. E. (Eds.). (1999). "Directional Statistics" Hoboken, NJ, USA: John Wiley & Sons, Inc. doi: 10.1002/9780470316979. [^4]: Stephan, T., & Enkelmann, E. (2025). All Aligned on the Western Front of North America? Analyzing the Stress Field in the Northern Cordillera. Tectonics, 44(9). $$D = \frac{1}{Z} \sum_{i=1}^{n} w_i d_i$$ where $n$ s the number if observations, $w_i$ are weights of each observation, and $Z$ is the sum of all weights $Z=\sum_{i=1}^{n} w_i$. It can be measured using `circular_dispersion()`: ```{r dispersion, echo=TRUE} circular_dispersion(san_andreas.por$azi.PoR, y = 135, w = w) ``` The dispersion parameter yields a number in the range between 0 and 1 which indicates the quality of the fit. Low dispersion values ($D \le 0.15$) indicate good agreement between predicted and observed directions (angle difference $\le 22.5^\circ$ for axial data). High values ($D > 0.5$) indicate a systematic misfit between predicted and observed directions of about $> 45^\circ$ (axial data). A misfit of $90^\circ$ and/or a random distribution of results in $D = 1$. The standard error and the confidence interval of the calculated circular dispersion can be estimated by bootstrapping via: ```{r dispersion_MLE, echo=TRUE} circular_dispersion_boot(san_andreas.por$azi.PoR, y = 135, w = w, R = 1000) ``` ### Rayleigh Test The statistical test for the goodness-of-fit is the (weighted) **Rayleigh Test**[^5] with a specified mean direction (here the predicted direction of $135^{\circ}$): [^5]: Stephan, T., & Enkelmann, E. (2025). All Aligned on the Western Front of North America? Analyzing the Stress Field in the Northern Cordillera. Tectonics, 44(9). ```{r rayleigh2, echo=TRUE} weighted_rayleigh(san_andreas.por$azi.PoR, mu = 135, w = w) ``` Here, the Null Hypothesis is rejected, and thus, the alternative, i.e. an non-uniform distribution with the predicted direction as the mean cannot be excluded. ## Orientation tensor For axial orientation data, summary statistics can also be expressed by the 2D orientation tensor. The orientation tensor is related to the moment of inertia given, that is minimized by calculating the Cartesian coordinates of the orientation data, and calculating their covariance matrix: $$ I = \left[ \begin{array}{@{}cc@{}} s_x^2 & s_{x,y} \\ s_{y,x} & s_y^2 \end{array} \right] = \left[ \begin{array}{@{}cc@{}} \frac{1}{n}\sum\limits_{i=1}^{n} (x_i-0)^2 & \frac{1}{n}\sum\limits_{i=1}^{n} (x_i-0)(y_i-0) \\ \frac{1}{n}\sum\limits_{i=1}^{n} (y_i-0)(x_i-0) & \frac{1}{n}\sum\limits_{i=1}^{n} (y_i-0)^2 \end{array} \right] = \frac{1}{n} \left[ \begin{array}{@{}cc@{}} \sum\limits_{i=1}^{n} x_i^2 & \sum\limits_{i=1}^{n} x_iy_i \\ \sum\limits_{i=1}^{n} x_iy_i & \sum\limits_{i=1}^{n} y_i^2 \end{array} \right] = \frac{1}{n} \sum\limits_{i=1}^{n} \left[ \begin{array}{@{}c@{}} x_i \\ y_i \end{array} \right] \left[ \begin{array}{@{}cc@{}} x_i & y_i \end{array} \right] $$ Orientation tensor $T$ and the inertia tensor $I$ are related by $I = E - T$ where $E$ denotes the unit matrix, so that $$T = \frac{1}{n} \sum_{i=i}^{n} x_i \cdot x_i^\intercal$$. The spectral decomposition of the 2D orientation tensor into two Eigenvectors and corresponding Eigenvalues provides provides a measure of location and a corresponding measure of dispersion, respectively. The function `ot_eigen2d()`calculates the orientation tensor and extracts the Eigenvalues and Eigenvectors. The function accepts the weightings of the data: ```{r otensor} ot_eigen2d(san_andreas.por$azi.PoR, w) ``` The **Eigenvalues** ($\lambda_1 > \lambda_2$) can be interpreted as the fractions of the variance explained by the orientation of the associated Eigenvectors. The two perpendicular **Eigenvectors** ($a_1, a_2$) are the "principal directions" with respect to the highest and the lowest concentration of orientation data. The strength of the orientation is the largest Eigenvalue $\lambda_1$ normalized by the sum of the eigenvalues. The smallest Eigenvalue $\lambda_2$ is a **measure of dispersion** of 2D orientation data with respect to $a_1$. # References Bachmann, F., Hielscher, R., Jupp, P. E., Pantleon, W., Schaeben, H., & Wegert, E. (2010). Inferential statistics of electron backscatter diffraction data from within individual crystalline grains. Journal of Applied Crystallography, 43(6), 1338–1355. Mardia, K. V., and Jupp, P. E. (Eds.). (1999). "Directional Statistics" Hoboken, NJ, USA: John Wiley & Sons, Inc. Stephan, T., & Enkelmann, E. (2025). All Aligned on the Western Front of North America? Analyzing the Stress Field in the Northern Cordillera. Tectonics, 44(9). Ziegler, Moritz O., and Oliver Heidbach. 2017. "Manual of the Matlab Script Stress2Grid" GFZ German Research Centre for Geosciences; World Stress Map Technical Report 17-02. doi: [10.5880/wsm.2017.002](https://doi.org/10.5880/wsm.2017.002).