plspy is a Partial Least Squares package developed to replicate and extend the PLS MATLAB package created by Randy McIntosh, et al for use in neuroimaging applications. .
Checkout the documentation for plspy at https://plspy.readthedocs.io/en/latest/
The following steps will download and install plspy to your computer:
pip install plspy
If you prefer to build from source, run these commands:
git clone https://github.com/McIntosh-LabI/plspy.git
cd plspy
python setup.py install
Basic usage examples: Note: There are 3 required arguments, used in the following order:
1) X - 2-d task matrix
2) a list containing the number of subjects in each group
3) argument 3 is an int indicating the number of conditions
Example arguments are used below.
Mean-Centred Task PLS:
>>> result = plspy.PLS(X, [10, 10], 3, num_perm=500, num_boot=500, pls_method="mct")
Behavioural PLS:
>>> result = plspy.PLS(X, [10, 10], 3, Y=Y, pls_method="rb")
Contrast Task PLS:
>>> result = plspy.PLS(X, [10, 10], 3, contrasts=C, pls_method="cst")
Contrast Behavioural PLS:
>>> result = plspy.PLS(X, [10, 10], 3, Y=Y, contrasts=C, pls_method="csb")
Multiblock PLS:
>>> result = plspy.PLS(X, [10, 10], 3, Y=Y, pls_method="mb")
To see documentation on additional arguments and fields available, call help on a specific PLS method (see below for details). Documentation is available both in help() form and will also be available in website form. More information on how to access online documentation is forthcoming. Information on how to use help() is below. To get help documentation on a particular version of PLS, type the following in a Python interpreter after loading the module:
>>> import plspy
>>> help(plspy.methods["<methodname>"])
Where is the string of one of the PLS versions shown below.
Available methods:
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"mct" - Mean-Centred Task PLS
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"rb" - Regular Behaviour PLS
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"cst" - Contrast Task PLS
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"csb" - Contrast Behaviour PLS
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"mb" - Multiblock PLS
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"cmb" - Contrast Multiblock PLS (under construction)