An S4 class to represent a multivariate mixture of regression model, extends icl_model-class. The model follows minka-linear . The model corresponds to the following generative model: $$\pi \sim Dirichlet(\alpha)$$ $$Z_i \sim \mathcal{M}(1,\pi)$$ $$V_k \sim \mathcal{W}(\varepsilon^{-1},n_0)$$ $$A_k \sim \mathcal{MN}(0,(V_k)^{-1},\tau X^{t}X)$$ $$Y_{i.}|X_{i.}Z_{ik}=1 \sim \mathcal{N}(A_kx_{i.},V_{k}^{-1})$$ with $$\mathcal{W}(\epsilon^{-1},n_0)$$ the Whishart distribution and $$\mathcal{MN}$$ the matrix-normal distribution.

## Slots

name

name of the model

alpha

Dirichlet over cluster proportions prior parameter (default to 1)

tau

Prior parameter (inverse variance) default 0.01

epsilon

Covariance matrix prior parameter (default to NaN, in this case epsilon will be fixed to a diagonal variance matrix equal to 0.1 time the variance of the regression residuals with only one cluster.)

N0

Prior parameter (default to NaN, in this case N0 will be fixed equal to the number of columns of Y.)

## Examples

new("mvmreg")
#> An object of class "mvmreg"
#> Slot "tau":
#> [1] 0.01
#>
#> Slot "N0":
#> [1] NaN
#>
#> Slot "epsilon":
#>      [,1]
#> [1,]  NaN
#>
#> Slot "name":
#> [1] "mvmreg"
#>
#> Slot "alpha":
#> [1] 1
#> new("mvmreg",alpha=1,tau=0.1,N0=15)
#> An object of class "mvmreg"
#> Slot "tau":
#> [1] 0.1
#>
#> Slot "N0":
#> [1] 15
#>
#> Slot "epsilon":
#>      [,1]
#> [1,]  NaN
#>
#> Slot "name":
#> [1] "mvmreg"
#>
#> Slot "alpha":
#> [1] 1
#>