Recently, I presented R code for the shift function, a powerful tool to compare two entire marginal distributions.

The Matlab code is now available on github.

`shifthd`

has the same name as its R version, which was originally programmed by Rand Wilcox and first documented in 1995 (see details ). It computes a shift function for independent groups, using a percentile bootstrap estimation of the SE of the quantiles to compute confidence intervals.

`shiftdhd`

is the version for dependent groups.

More recently, Wilcox introduced a new version of the shift function in which a straightforward percentile bootstrap is used to compute the confidence intervals, without estimation of the SE of the quantiles. This is implemented in Matlab as `shifthd_pbci`

for independent groups (equivalent to `qcomhd`

in R); as `shiftdhd_pbci`

for dependent groups (equivalent to `Dqcomhd`

in R).

A demo file `shift_function_demo`

is available here, along with the function `shift_fig`

and dependencies `cmu`

and `UnivarScatter`

.

For instance, if we use the ozone data covered in the previous shift function post, a call to `shifthd`

looks like this:

[xd, yd, delta, deltaCI] = shifthd(control,ozone,200,1);

producing this figure:

The output of `shifthd`

, or any of the other 3 sf functions, can be used as input into `shift_fig`

:

shift_fig(xd, yd, delta, deltaCI,control,ozone,1,5);

producing this figure:

This is obviously work in progress, and `shift_fig`

is meant as a starting point.

Have fun exploring how your distributions differ!

And if you have any question, donâ€™t hesitate to get in touch.