cov.independent.d: Derivative of the independent covariance function. Does not...
In JimSkinner/stpca: Structured PCA
View source: R/covariance-functions.R
Description
Derivative of the independent covariance function. Does not depend
on the value of σ^2_k; is always 1 everywhere the inputs have
zero distance, and zero everywhere else.
Usage
1
cov.independent.d(X, X2, beta, D = NA, ...)
Arguments
X
Matrix of data
X2
(optional) second matrix of data; if omitted, X is used.
beta
The single hyperparameter: the log of the signal variance
JimSkinner/stpca documentation built on May 28, 2019, 12:44 p.m.
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View source: R/covariance-functions.R
Description
Derivative of the independent covariance function. Does not depend on the value of σ^2_k; is always 1 everywhere the inputs have zero distance, and zero everywhere else.
Usage
1 | cov.independent.d(X, X2, beta, D = NA, ...)
|
Arguments
X |
Matrix of data |
X2 |
(optional) second matrix of data; if omitted, X is used. |
beta |
The single hyperparameter: the log of the signal variance |
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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