Multivariate mixture of regression model description class

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
#>