Description Usage Arguments Value References
View source: R/suffDimReduct.R
This implements the same method of regularization used in RSIR
to improve the stability
of SAVE
in high dimensional scenarios.
1 2 3 4 5 6 7 8 9 |
formula |
a model formula |
data |
a data frame |
rank |
the desired number of sufficient predictors to return. the default is "all". |
slices |
the number of slices into which the response variable should be split. defaults to 5. for categorical response variables the maximum allowed is the number of response levels minus one. if set above this, it is silently adjusted. |
ytype |
either numeric or categorical |
target |
one of "ident", "const", or "pca". "ident" uses as the shrinkage target the identity matrix, which yields the ridge penalty, (x'x + Iλ)^{-1}. "const" uses as the shrinakge target a matrix with the geometric mean of the variances along the diagonal, and the mean covariance in each off-diagonal cell. "pca" |
lambda |
the regularization parameter. note that the value selected will have quite different impacts depending on the shrinkage target. |
an sdr object
Cook, R.D., Weisberg, S. (1991) Sliced Inverse Regression for Dimension Reduction: Comment.
Journal of the American Statistical Association, 86(414):328-332
Zhong, W., Zeng, P., Ma, P., Liu, J.S., & Zhu, M.Y. (2005). RSIR: regularized sliced inverse regression
for motif discovery. Bioinformatics, 21 22, 4169-75.
Bernard-Michel, C., Gardes, L., & Girard, S. (2009a). Gaussian Regularized Sliced Inverse Regression.
Statistics and Computing, 19, 85-98.
Bernard‐Michel, C., Douté, S., Fauvel, M., Gardes, L., & Girard, S. (2009b). Retrieval of Mars surface
physical properties from OMEGA hyperspectral images using regularized sliced inverse regression, J. Geophys.
Res., 114, E06005, doi:10.1029/2008JE003171.
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