CVlm: Leave one-subject cross-validation score for local linear fit
In npmlda: Nonparametric Models for Longitudinal Data
Description
Leave one-subject cross-validation score for local linear fit
Usage
1
CVlm(Xvec, Yvec, bw, ID, Wt)
Arguments
Xvec, Yvec
numeric vectors of data values, Xvec and Yvec must have the same length.
bw
a bandwidth of the Epanechnikov kernel
ID
subject ID of the data value
Wt
a weight vector, may be subject-specific. a weight vector or a constant. For longitudinal data, Wt=1/N corresponds to measurement uniform weight and Wt=1/(nni) corresponds subject uniform weight.
npmlda documentation built on May 2, 2019, 12:54 a.m.
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Description
Leave one-subject cross-validation score for local linear fit
Usage
1 | CVlm(Xvec, Yvec, bw, ID, Wt)
|
Arguments
Xvec, Yvec |
numeric vectors of data values, Xvec and Yvec must have the same length. |
bw |
a bandwidth of the Epanechnikov kernel |
ID |
subject ID of the data value |
Wt |
a weight vector, may be subject-specific. a weight vector or a constant. For longitudinal data, Wt=1/N corresponds to measurement uniform weight and Wt=1/(nni) corresponds subject uniform weight. |
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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