ADchol: construct object for Automated Differentiation Cholesky...
In LMMsolver: Linear Mixed Model Solver
View source: R/DifferentiationCholesky.R
ADchol R Documentation
construct object for Automated Differentiation Cholesky decomposition
Description
Construct object for reverse Automated Differentiation of Cholesky decomposition,
with as input a list of semi-positive symmetric sparse matrices P_i, each of
dimension q \times q. The function ADchol calculates the matrix C, the sum
the precision matrices P_i: C = \sum_{i} P_i. Next, it calculates the Cholesky
Decomposition using the multiple minimum degree (MMD) algorithm
of the spam package.
Usage
ADchol(lP)
Arguments
lP
a list of symmetric matrices of class spam, each of dimension q \times q,
and with sum of the matrices assumed to be positive definite.
Value
An object of class ADchol. This object is used to calculate the partial
partial derivatives of log|C| in an efficient way.
References
Furrer, R., & Sain, S. R. (2010). spam: A sparse matrix R package with emphasis
on MCMC methods for Gaussian Markov random fields.
Journal of Statistical Software, 36, 1-25.
LMMsolver documentation built on Sept. 11, 2024, 7:31 p.m.
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View source: R/DifferentiationCholesky.R
| ADchol | R Documentation |
construct object for Automated Differentiation Cholesky decomposition
Description
Construct object for reverse Automated Differentiation of Cholesky decomposition,
with as input a list of semi-positive symmetric sparse matrices P_i, each of
dimension q \times q. The function ADchol calculates the matrix C, the sum
the precision matrices P_i: C = \sum_{i} P_i. Next, it calculates the Cholesky
Decomposition using the multiple minimum degree (MMD) algorithm
of the spam package.
Usage
ADchol(lP)
Arguments
lP |
a list of symmetric matrices of class spam, each of dimension |
Value
An object of class ADchol. This object is used to calculate the partial
partial derivatives of log|C| in an efficient way.
References
Furrer, R., & Sain, S. R. (2010). spam: A sparse matrix R package with emphasis on MCMC methods for Gaussian Markov random fields. Journal of Statistical Software, 36, 1-25.
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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