conjugateLinearModel | R Documentation |
See details for model. Notation: N
is number of samples,
D
is the dimension of the response, Q
is number
of covariates.
conjugateLinearModel(Y, X, Theta, Gamma, Xi, upsilon, n_samples = 2000L)
Y |
matrix of dimension D x N |
X |
matrix of covariates of dimension Q x N |
Theta |
matrix of prior mean of dimension D x Q |
Gamma |
covariance matrix of dimension Q x Q |
Xi |
covariance matrix of dimension D x D |
upsilon |
scalar (must be > D-1) degrees of freedom for InvWishart prior |
n_samples |
number of samples to draw (default: 2000) |
Y \sim MN_{D-1 \times N}(\Lambda \mathbf{X}, \Sigma, I_N)
\Lambda \sim MN_{D-1 \times Q}(\Theta, \Sigma, \Gamma)
\Sigma \sim InvWish(\upsilon, \Xi)
This function provides a means of sampling from the posterior distribution of
Lambda
and Sigma
.
List with components
Lambda Array of dimension (D-1) x Q x n_samples (posterior samples)
Sigma Array of dimension (D-1) x (D-1) x n_samples (posterior samples)
sim <- pibble_sim()
eta.hat <- t(alr(t(sim$Y+0.65)))
fit <- conjugateLinearModel(eta.hat, sim$X, sim$Theta, sim$Gamma,
sim$Xi, sim$upsilon, n_samples=2000)
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