Diagonal Gaussian mixture model description class

An S4 class to represent a multivariate diagonal Gaussian mixture model, extend icl_model-class. The model corresponds to the following generative model: $$ \pi \sim Dirichlet(\alpha)$$ $$ Z_i \sim \mathcal{M}(1,\pi)$$ $$ \lambda_k^{(d)} \sim \mathcal{G}(\kappa,\beta)$$ $$ \mu_k^{(d)} \sim \mathcal{N}(\mu,(\tau \lambda_k)^{-1})$$ $$ X_{i.}|Z_{ik}=1 \sim \mathcal{N}(\mu_k,\lambda_{k}^{-1})$$ with $\mathcal{G}(\kappa,\beta)$ the Gamma distribution with shape parameter $\kappa$ and rate parameter $\beta$.

Slots

name: name of the model
alpha: Dirichlet over cluster proportions prior parameter (default to 1)
tau: Prior parameter (inverse variance), (default 0.01)
kappa: Prior parameter (gamma shape), (default to 1)
beta: Prior parameter (gamma rate), (default to NaN, in this case beta will be estimated from data as 0.1 time the mean of X columns variances)
mu: Prior for the means (vector of size D), (default to NaN, in this case mu will be estimated from data as the mean of X)

References

Bertoletti, Marco & Friel, Nial & Rastelli, Riccardo. (2014). Choosing the number of clusters in a finite mixture model using an exact Integrated Completed Likelihood criterion. METRON. 73. 10.1007/s40300-015-0064-5. #'

Examples

new("diaggmm")
#> An object of class "diaggmm"
#> Slot "tau":
#> [1] 0.01
#> 
#> Slot "kappa":
#> [1] 1
#> 
#> Slot "beta":
#> [1] NaN
#> 
#> Slot "mu":
#> [1] NaN
#> 
#> Slot "name":
#> [1] "diaggmm"
#> 
#> Slot "alpha":
#> [1] 1
#> 
new("diaggmm",alpha=1,tau=0.1,beta=0.1)
#> An object of class "diaggmm"
#> Slot "tau":
#> [1] 0.1
#> 
#> Slot "kappa":
#> [1] 1
#> 
#> Slot "beta":
#> [1] 0.1
#> 
#> Slot "mu":
#> [1] NaN
#> 
#> Slot "name":
#> [1] "diaggmm"
#> 
#> Slot "alpha":
#> [1] 1
#>