Simulated univariate one-dimensional datasets
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
.gitignore
LICENSE.txt
README.md
fig_datasetsABC.png
simscalar01.csv.zip
simscalar01.h5.zip

README.md

data1d

data1d: Simulated univariate one-dimensional datasets for demonstrating continuum-level statistical analysis.

Overview

These three datasets were generated to illustrate the basic concepts of random field theory-based statistical inference. Each dataset contains ten scalar 1D trajectories, each with 101 nodes.

  • Datasets A and B represent one-sample (or paired) designs.
  • Dataset C represents a regression design, with the following independent variable values: [50, 53.75, 57.5, 61.25, 65, 75, 78.75, 82.5, 86.25, 90]

Data

simscalar01.h5.zip (HDF5 format) simscalar01.csv.zip (CSV format)

Reference

Pataky TC, Vanrenterghem J, Robinson MA (2015). Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis. Journal of Biomechanics 48(7): 1277-1285.

Loading and viewing the data (MATLAB)

file_name = 'datasetA.h5'; 
y = hdf5read(file_name, '/y'); 
plot(y)

Loading and viewing the data (Python)

import tables 
from matplotlib import pyplot 

file_name = 'datasetA.h5' 
file_id = tables.openFile(file_name, mode='r') 
y = file_id.getNode('/y').read() 
file_id.close() 

pyplot.plot(y.T) 
pyplot.show()

Data format

Please find more information about the HDF5 format at: www.hdfgroup.org/HDF5 (for Matlab, Python, C, etc.)

Copyright

Copyright (c) 2014 Todd Colin Pataky, Jos Vanrenterghem, and Mark Robinson