getSigmaUY: Returns the covariance matrix of t(U) if model is simple...
In JonasWallin/PolyMixed:
getSigmaUY R Documentation
Returns the covariance matrix of t(U)
if model is simple return the diagonal vector
@param theta (3 x 1) \sigma_Y, \tau, \sigma_U
@param likobj (list) contaning:
Yu (n x 1) the data (modifed by U in the SVD(X))
Xu (n x k + 1) the covariates, for \beta, \mu_0 (modifed by U in the SVD(X))
H (n x n_c) covariance matrix
HHt (n x n)
SVDX (list) singular value decomposition of X
Description
Returns the covariance matrix of t(U)
if model is simple return the diagonal vector
@param theta (3 x 1) \sigma_Y, \tau, \sigma_U
@param likobj (list) contaning:
Yu (n x 1) the data (modifed by U in the SVD(X))
Xu (n x k + 1) the covariates, for \beta, \mu_0 (modifed by U in the SVD(X))
H (n x n_c) covariance matrix
HHt (n x n)
SVDX (list) singular value decomposition of X
Usage
getSigmaUY(theta, likObj)
JonasWallin/PolyMixed documentation built on April 8, 2023, 4:26 p.m.
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| getSigmaUY | R Documentation |
Returns the covariance matrix of t(U) if model is simple return the diagonal vector @param theta (3 x 1) \sigma_Y, \tau, \sigma_U @param likobj (list) contaning: Yu (n x 1) the data (modifed by U in the SVD(X)) Xu (n x k + 1) the covariates, for \beta, \mu_0 (modifed by U in the SVD(X)) H (n x n_c) covariance matrix HHt (n x n) SVDX (list) singular value decomposition of X
Description
Returns the covariance matrix of t(U) if model is simple return the diagonal vector @param theta (3 x 1) \sigma_Y, \tau, \sigma_U @param likobj (list) contaning: Yu (n x 1) the data (modifed by U in the SVD(X)) Xu (n x k + 1) the covariates, for \beta, \mu_0 (modifed by U in the SVD(X)) H (n x n_c) covariance matrix HHt (n x n) SVDX (list) singular value decomposition of X
Usage
getSigmaUY(theta, likObj)
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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