sir: Sliced Inverse Regression
In HarrisQ/linearsdr: Linear Sufficient Reduction Methods
sir R Documentation
Sliced Inverse Regression
Description
This conducts a sliced inverse regression as in Li (2018) with modifications
to improve speed and to allow for the option of standardizing and regularizing
Usage
sir(x, y, nslices, d, ytype, lambda = 0)
Arguments
x
a 'n x p' matrix of predictors; n sample size, p dimension
y
a scalar response
nslices
specify the number of slices to conduct;
d
specify the reduced dimension
ytype
specify the response as 'continuous' or 'categorical'
lambda
a L2 or Tikonov regularizer for the sample covariance matrix;
default is '0', i.e. no regularization
Details
Standardizing is the default as it is necessary for recovering the properly scaled
central subspace. However, in certain contexts, the standardization is not necessary,
and so we leave this option open to the practitioner.
The L2-regularization option corresponds to the SIR regularization idea
by Zhang et al.(2005).
Value
A list containing both the estimate and candidate matrix.
beta - A 'pxd' matrix that estimates a basis for the central subspace.
cand_mat - The candidate matrix for SIR; this is used in other functions
for order determination.
HarrisQ/linearsdr documentation built on Nov. 29, 2022, 12:22 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| sir | R Documentation |
Sliced Inverse Regression
Description
This conducts a sliced inverse regression as in Li (2018) with modifications to improve speed and to allow for the option of standardizing and regularizing
Usage
sir(x, y, nslices, d, ytype, lambda = 0)
Arguments
x |
a 'n x p' matrix of predictors; n sample size, p dimension |
y |
a scalar response |
nslices |
specify the number of slices to conduct; |
d |
specify the reduced dimension |
ytype |
specify the response as 'continuous' or 'categorical' |
lambda |
a L2 or Tikonov regularizer for the sample covariance matrix; default is '0', i.e. no regularization |
Details
Standardizing is the default as it is necessary for recovering the properly scaled central subspace. However, in certain contexts, the standardization is not necessary, and so we leave this option open to the practitioner.
The L2-regularization option corresponds to the SIR regularization idea by Zhang et al.(2005).
Value
A list containing both the estimate and candidate matrix.
beta - A 'pxd' matrix that estimates a basis for the central subspace.
cand_mat - The candidate matrix for SIR; this is used in other functions for order determination.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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