This release addresses a single bug. There are no known breaking changes to the API; hence all users are strongly encouraged to upgrade to the latest version.
- Bug-fixes:
- Bug affecting display of Tufte gapped lines in Cumming plots if the supplied :py
pandas
:pyDataFrame
was in 'wide' format, but did not have equal number of Ns in the groups. (Issue #79)
- Bug affecting display of Tufte gapped lines in Cumming plots if the supplied :py
- Feature additions:
- It is now possible to specify a pre-determined :py
matplotlib
:pyAxes
to create the estimation plot in. See the new section in the tutorial for more information. (Pull request #73; thanks to Adam Nekimken (@anekimken).
- It is now possible to specify a pre-determined :py
- Bug-fixes:
- Ensure all dependencies are installed along with DABEST. (Pull request #71; thanks to Matthew Edwards (@mje-nz).
- Handle infinities in bootstraps during plotting. (Issue #72, Pull request #74)
- Feature additions:
- Bug-fixes:
- Bug affecting calculation of paired difference confidence intervals. (Issue #48 in ACCLAB/dabestr)
- NaNs in unused/unrelated columns would result in null results (Issue #44)
- 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
This release fixes a bug that did not handle when the supplied x
was a :pypandas
:pyCategorical
object, but the idx
did not include all the original categories.
This release fixes a bug that has a mean difference or median difference of exactly 0. (Pull request #73; thanks to Mason Malone (@MasonM).
This release fixes a bug that misplotted the gapped summary lines in Cumming plots when the x-variable was a :pypandas
:pyCategorical
object.
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()
.
my_data = dabest.load(my_dataframe, idx=("Control", "Test"))
This creates a :pyDabest
object with effect sizes as instances.
my_data.mean_diff
which prints out:
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:
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.
my_data.mean_diff.plot()
See the tutorial
and api
for more details, including keyword options for the load()
and plot()
methods.
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.
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.
Fix bug that wasn't updating the seaborn version upon setup and install.
Update dependencies to
- numpy 1.15
- scipy 1.1
- matplotlib 2.2
- seaborn 0.9
Aesthetic changes
- add
tick_length
andtick_pad
arguments to allow tweaking of the axes tick lengths, and padding of the tick labels, respectively.
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
Update dependencies to
- numpy v1.13
- scipy v1.0
- pandas v0.22
- seaborn v0.8