Welcome to plspy’s documentation!
plspy
plspy is a Partial Least Squares package developed at the Institute for Neuroscience and Neurotechnology at Simon Fraser University.
In addition to core PLS functionality, this package also contains the following modules:
- class_functions
core PLS functions, such as mean-centring, calling SVD/GSVD, etc.
- gsvd
Implementation of GSVD that uses NumPy’s Fortran SVD function
- resample
Functions used for resampling in permutation and bootstrap
- bootstrap_permutation
Houses the bootstrap and permutation object (called by PLS)
- pls_classes
Source code for each PLS version
- exceptions
Houses custom exceptions used within PLS
Basic usage examples:
Note: There are 3 required arguments, used in the following order:
X - 2-d task matrix
a list containing the number of subjects in each group
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 <method> is the string of one of the PLS versions shown below.
Available methods:
“mct” - Mean-Centred Task PLS
“rb” - Regular Behaviour PLS
“cst” - Contrast Task PLS
“csb” - Contrast Behaviour PLS
“mb” - Multiblock PLS
“cmb” - Contrast Multiblock PLS (under construction)
- Note: calling
>>> help(plspy.PLS)
will show you this same help page.
Author: Noah Frazier-Logue
Contents: