delta.K: Give the gradient for a diagnoal matrix (K) w.r.t. its...
In feng-li/movingknots: Efficient Bayesian multivariate surface regression
Description
Usage
Arguments
Value
Author(s)
View source: R/models__linear__etc__delta.K.R
Description
Details are available in the paper.
Usage
1
Arguments
diag.K
NA
power
"scalar". The power for K to obtain the gradient for vec(K^(power))
w.r.t. diag(K)'.
K
"matrix". The diagonal matrix. p-by-p
Value
"matrix". The gradient, pp-by-p
Author(s)
Feng Li, Department of Statistics, Stockholm University, Sweden.
feng-li/movingknots documentation built on March 30, 2021, 11:58 a.m.
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Description Usage Arguments Value Author(s)
View source: R/models__linear__etc__delta.K.R
Description
Details are available in the paper.
Usage
1 |
Arguments
diag.K |
NA |
power |
"scalar". The power for K to obtain the gradient for vec(K^(power)) w.r.t. diag(K)'. |
K |
"matrix". The diagonal matrix. p-by-p |
Value
"matrix". The gradient, pp-by-p
Author(s)
Feng Li, Department of Statistics, Stockholm University, Sweden.
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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