svdRegression: Linear regression using a singular value decomposition
In driegert/transfer: Frequency Domain Transfer Function Estimation Tools
Description
Usage
Arguments
Details
Value
Description
Performs a multivariate linear regression using a singular value decomposition.
Usage
1
svdRegression(x, y)
Arguments
x
A matrix or vector containing the predictors.
y
A vector containing the response.
Details
Uses the process described in Mandel (1982), "Use of the Singular value
Decomposition in Regression Analysis".
Value
A list containing the regression coefficients corresponding to the
columns of x, the standard error estimates (*not* the variance) on those coefficients,
and the eigenvalues of the decomposition (singular values squared).
driegert/transfer documentation built on May 15, 2019, 2:11 p.m.
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Description Usage Arguments Details Value
Description
Performs a multivariate linear regression using a singular value decomposition.
Usage
1 | svdRegression(x, y)
|
Arguments
x |
A |
y |
A |
Details
Uses the process described in Mandel (1982), "Use of the Singular value Decomposition in Regression Analysis".
Value
A list containing the regression coefficients corresponding to the
columns of x, the standard error estimates (*not* the variance) on those coefficients,
and the eigenvalues of the decomposition (singular values squared).
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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