Built-in datasets¶

spm1d comes packages with a variety of 0D and 1D datasets located in ./spm1d/data.

This document provides a brief overview of the built-in datasets.

To explore the datasets please use the scripts in spm1d/examples.

Loading datasets¶

All datasets can be accessed using the following syntax:

>>> dataset = spm1d.data.uv0d.anova1.Cars()
>>> data = dataset.get_data()

where:

spm1d.data.uv0d refers to univariate zero-dimensional data

spm1d.data.uv0d.anova1 refers to the type of statistical test

dataset.get_data() extracts only the variable needed for the given test

NOTES:

For access to univariate 1D datasets, and multivariate 0D and 1D datasets, use spm1d.data.uv1d, spm1d.data.mv0d and spm1d.data.mv1d, respectively.
The datasets contain a variety of other variables including web links and expected results.

Dataset details¶

spm1d no longer provides detailed dataset descriptions.

Instead users should consult the references and web links provided with each dataset as follows:

>>> dataset = spm1d.data.uv0d.anova1.Cars()
>>> print( dataset )

The result will look something like this:

Dataset
        Name      : "Cars"
        Design    :  One-way ANOVA
        Data dim  :  0
        Web       :  http://cba.ualr.edu/smartstat/topics/anova/example.pdf
(Expected results)
        F         :  25.17
        df        :  (2, 6)
        p         :  0.001207

Checking expected results¶

The expected results shown in the example above can be corroborated against spm1d’s calculations as follows:

>>> dataset = spm1d.data.uv0d.anova1.Cars()
>>> Y,A     = dataset.get_data()
>>> F       = spm1d.stats.anova1(Y, A, equal_var=True)
>>> Fi      = F.inference(0.05)
>>> print( Fi )

Here Y and A are both 9-component vectors, where Y represents the observations and where A continains integers which indicate groups.

The spm1d result is:

SPM{F} (0D) inference
        SPM.z        :  25.17541
        SPM.df       :  (2, 6)
Inference:
        SPM.alpha    :  0.050
        SPM.zstar    :  5.14325
        SPM.h0reject :  True
        SPM.p        :  0.00121

Note that the F statistic (SPM.z), degrees of freedom (SPM.df) and p value (SPM.p) match the expected dataset results.

The critical test statistic threshold at alpha (SPM.zstar) is not typically reported, but is an essential component of 1D analyses so is also presented in 0D results for comparisons between 0D and 1D critical thresholds.

Notes on 1D datasets¶

Warning

Only 0D datasets contain expected results.

For one-dimensional result verifications refer to the references provided.

For example:

>>>  dataset      = spm1d.data.uv1d.anova2.Besier2009kneeflexion()
>>>  Y,A,B        = dataset.get_data()  #A:foot, B:speed
>>>  print( dataset )

This will yield:

Dataset
        Name      : "Besier2009kneeflexion"
        Design    :  Two-way ANOVA
        Data dim  :  1
        Reference :  Besier, T. F., Fredericson, M., Gold, G. E., Beaupré, G. S., & Delp, S. L. (2009). Knee muscle forces during walking and running in patellofemoral pain patients and pain-free controls. Journal of Biomechanics, 42(7), 898–905. http://doi.org/10.1016/j.jbiomech.2009.01.032
        Web       :  https://simtk.org/home/muscleforces
        Data file :  /Users/todd/Documents/Python/myLibraries/spm1d/data/datafiles/Besier2009kneeflexion.npz
        Results   :  Pataky, T. C., Vanrenterghem, J., & Robinson, M. A. (2015). Two-way ANOVA for scalar trajectories, with experimental evidence of non-phasic interactions. Journal of Biomechanics, 48(1), 186–189. http://doi.org/10.1016/j.jbiomech.2014.10.013
(Expected results)
        F          :  None
        df         :  None
        p          :  None