CR: Contour Regression
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities
Description
Usage
Arguments
Value
References
View source: R/suffDimReduct.R
Description
The idea behind contour regression (CR) is that when considering a surface S the
central dimension reduction subspace represents the directions in which the height of S changes the most.
In other words, the central subspace is aligned with the gradients of S. Gradient directions are orthogonal
to the contours, where the height of S remains constant. Hence, the central subspace can be approximated by finding the
orthogonal complement to the contour directions (Li, Zha, & Chiaromonte, 2005).
Usage
1
Arguments
formula
a model formula
data
a data frame
rank
the desired number of sufficient predictors to return. the default is "all".
pct
the span. should be a number greater than zero and less than one.
Value
an sdr object
References
Li, B.; Zha, H.; Chiaromonte, F. (2005) Contour regression: A general approach to dimension reduction. Ann. Statist. 33, 4, 1580-1616. doi:10.1214/009053605000000192.
abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.
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Description Usage Arguments Value References
View source: R/suffDimReduct.R
Description
The idea behind contour regression (CR) is that when considering a surface S the central dimension reduction subspace represents the directions in which the height of S changes the most. In other words, the central subspace is aligned with the gradients of S. Gradient directions are orthogonal to the contours, where the height of S remains constant. Hence, the central subspace can be approximated by finding the orthogonal complement to the contour directions (Li, Zha, & Chiaromonte, 2005).
Usage
1 |
Arguments
formula |
a model formula |
data |
a data frame |
rank |
the desired number of sufficient predictors to return. the default is "all". |
pct |
the span. should be a number greater than zero and less than one. |
Value
an sdr object
References
Li, B.; Zha, H.; Chiaromonte, F. (2005) Contour regression: A general approach to dimension reduction. Ann. Statist. 33, 4, 1580-1616. doi:10.1214/009053605000000192.
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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