fit.mean: Penalized least square estimation with GCV
In funstatpackages/ImageSCC: SCC for Mean Function of Imaging Data
Description
The function selects penalty parameter via generalized
cross validation and solves penalized least square problem
in estimating mean functions with bivariate penalized splines.
Usage
1
Arguments
B
bivariate spline basis matrix.
Q2
qr decomposition of the smoothness matrix.
K
energy matrix.
lambda
candidate of the penalty parameter.
Y
a matrix of data with each row corresponding to one subject/image.
proj.matrix
a logical value indicating whether the projection matrix
will be returned for adjusting σ(z) in the construction of SCC.
funstatpackages/ImageSCC documentation built on March 3, 2020, 12:25 a.m.
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Description
The function selects penalty parameter via generalized cross validation and solves penalized least square problem in estimating mean functions with bivariate penalized splines.
Usage
1 |
Arguments
B |
bivariate spline basis matrix. |
Q2 |
qr decomposition of the smoothness matrix. |
K |
energy matrix. |
lambda |
candidate of the penalty parameter. |
Y |
a matrix of data with each row corresponding to one subject/image. |
proj.matrix |
a logical value indicating whether the projection matrix will be returned for adjusting σ(z) in the construction of SCC. |
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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