Self Calibrating Quantile-Quantile Plots for Visual Testing
dkay The density function of the K distribution
hideLocation Obfuscating the true location of the real data.
pkay The cumulative distribution function K distribution
qkay The K distribution quantile function
qqtest A self-calibrated quantile-quantile plot for assessing distri...
revealLocation Revealing locations encoded as a string calculation.
rkay The K distribution - generating pseudo-random values
Provides the function qqtest which incorporates uncertainty in its qqplot display(s) so that the user might have a better sense of the evidence against the specified distributional hypothesis. qqtest draws a quantile quantile plot for visually assessing whether the data come from a test distribution that has been defined in one of many ways. The vertical axis plots the data quantiles, the horizontal those of a test distribution. The default behaviour generates 1000 samples from the test distribution and overlays the plot with shaded pointwise interval estimates for the ordered quantiles from the test distribution. A small number of independently generated exemplar quantile plots can also be overlaid. Both the interval estimates and the exemplars provide different comparative information to assess the evidence provided by the qqplot for or against the hypothesis that the data come from the test distribution (default is normal or gaussian). Finally, a visual test of significance (a lineup plot) can also be displayed to test the null hypothesis that the data come from the test distribution.