Release Notes

v0.2.4

This release fixes the following issues:
  • Misalignment of Gardner-Altman plots when the dataset loaded is wide, but has NaNs in a column. (Issue #40)
  • Misalignment of Hedges’ g Gardner Altman plots (Also Issue #40).
  • Add groups_summaries_offset argument for better control over gapped Tufte line plotting. The default offset is now set at 0.1 as well. (Issue #35

v0.2.3

This release fixes a bug that did not handle when the supplied x was a pandas Categorical object, but the idx did not include all the original categories.

v0.2.2

This release fixes a bug that has a mean difference or median difference of exactly 0.

v0.2.1

This release fixes a bug that misplotted the gapped summary lines in Cumming plots when the x-variable was a pandas Categorical object.

v0.2.0

We have redesigned the interface from the ground up. This allows speed and flexibility to compute different effect sizes (including Cohen’s d, Hedges’ g, and Cliff’s delta). Statistical arguments are now parsed differently from graphical arguments.

In short, any code relying on v0.1.x will not work with v0.2.0, and must be upgraded.

Now, every analysis session begins with dabest.load().

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my_data = dabest.load(my_dataframe, idx=("Control", "Test"))

This creates a dabest object with effect sizes as instances.

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my_data.mean_diff

which prints out:

DABEST v0.2.0
=============

Good afternoon!
The current time is Mon Mar  4 17:03:29 2019.

The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.205, 0.774].

5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
The p-value(s) reported are the likelihood(s) of observing the effect size(s),
if the null hypothesis of zero difference is true.

The following are valid effect sizes:

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2
3
4
5
my_data.mean_diff
my_data.median_diff
my_data.cohens_d
my_data.hedges_g
my_data.cliffs_delta

To produce an estimation plot, each effect size instance has a plot() method.

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my_data.mean_diff.plot()

See the Tutorial and API for more details, including keyword options for the load() and plot() methods.

v0.1.7

The keyword cumming_vertical_spacing has been added to tweak the vertical spacing between the rawdata swarm axes and the contrast axes in Cumming estimation plots.

v0.1.6

Several keywords have been added to allow more fine-grained control over a selection of plot elements.

  • swarm_dotsize
  • difference_dotsize
  • ci_linewidth
  • summary_linewidth

The new keyword context allows you to set the plotting context as defined by seaborn’s plotting_context() .

Now, if paired=True, you will need to supply an id_col, which is a column in the DataFrame which specifies which sample the datapoint belongs to. See the Tutorial for more details.

v0.1.5

Fix bug that wasn’t updating the seaborn version upon setup and install.

v0.1.4

Update dependencies to

  • numpy 1.15
  • scipy 1.1
  • matplotlib 2.2
  • seaborn 0.9

Aesthetic changes

  • add tick_length and tick_pad arguments to allow tweaking of the axes tick lengths, and padding of the tick labels, respectively.

v0.1.3

Update dependencies to

  • pandas v0.23

Bugfixes

  • fix bug that did not label swarm_label if raw data was in tidy form
  • fix bug that did not dropnans for unpaired diff

v0.1.2

Update dependencies to

  • numpy v1.13
  • scipy v1.0
  • pandas v0.22
  • seaborn v0.8