Description Usage Arguments Details Value Examples
Construct a multivariate probit model object that stores information about model structure and parameters
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model |
An optional existing model object to be updated. |
response |
A matrix with n-by-d elements, where each row is a multivariate observation, see Details. A vector is interpreted as a single row matrix. |
X |
An optimally precomputed n-by-J model matrix, where J is the number regression coeficcients for each of the d dimensions. |
formula |
A formula interpretable by |
data |
A |
df |
Degrees of freedom for the normalised Wishart prior for the correlation matrix. See Details. |
prec_beta |
Prior precision for the regression coefficients |
The multivariate probit model has a multivariate binary response variable, here denoted Y. The model is built from a linear predictor
M = X B
where X is a n-by-J matrix of J predictors, and B is a J-by-d matrix of regression coefficients. Each row of M is the linear predictor for one multivariate observation. The response variables Y are linked to M by first defining latent Gaussian variables
Z=M+E
where each row of E is a multivariate Normal vector, E \sim N(0,Σ). Then,
Y_{i,k}=I(Z_{i,k} > 0).
Conditionally on B, each row of Y has a multinomial distribution on the set of all 0/1 combinations, with each probability equal to a hyperquadrant probability of a the multivariate Normal distribution N(μ,Σ), where μ is the corresponding row of M.
Only the inequality Y_{i,k} > 0 for the response variables is used, so alternative data representations such as -1/+1 will also work as expected.
The degrees of freedom for the normalised Wishart prior are linked to the
concentration pararameter η of the LKJ prior by the relation
df = 2 * eta + d - 1
, which makes the two models equivalent.
An object of class mp_model
1 2 3 4 5 6 | ## Not run:
if (interactive()) {
# EXAMPLE1
}
## End(Not run)
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