pred.msof.hd: Prediction for high dimensional linear multivariate...
In FRegSigCom: Functional Regression using Signal Compression Approach
Description
Usage
Arguments
Value
Author(s)
See Also
Examples
Description
Make predition for multivariate scalar response based on new observations of
large number of predictor curves from the CV object obtained by cv.msof.hd.
Usage
1
pred.msof.hd(fit.cv, X.test)
Arguments
fit.cv
the CV object obtained by cv.msof.hd.
X.test
new observations of functional predictors. It is a list of length p, the number of functional predcitors. Each element is the observed matrix from a functional predictor, with rows repsenting observation vectors and columns corresponding to the observation time points.
Value
A matrix which contains the predicted values of the multivariate
response. Each row is the prediction for each new observation.
Author(s)
Ruiyan Luo and Xin Qi
See Also
Examples
1
#See the examples in cv.msof.hd().
FRegSigCom documentation built on May 1, 2019, 9:45 p.m.
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Description Usage Arguments Value Author(s) See Also Examples
Description
Make predition for multivariate scalar response based on new observations of
large number of predictor curves from the CV object obtained by cv.msof.hd.
Usage
1 | pred.msof.hd(fit.cv, X.test)
|
Arguments
fit.cv |
the CV object obtained by |
X.test |
new observations of functional predictors. It is a list of length p, the number of functional predcitors. Each element is the observed matrix from a functional predictor, with rows repsenting observation vectors and columns corresponding to the observation time points. |
Value
A matrix which contains the predicted values of the multivariate response. Each row is the prediction for each new observation.
Author(s)
Ruiyan Luo and Xin Qi
See Also
Examples
1 | #See the examples in cv.msof.hd().
|
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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