Creates appropriate new normal block model depending on the parametrization

Creates appropriate new normal block model depending on the parametrization

Usage

get_model(
  data,
  blocks,
  sparsity = 0,
  zero_inflation = FALSE,
  control = NB_control()
)

Arguments

data

contains the matrix of responses (Y) and the design matrix (X).

blocks

either an integer (number of blocks), a vector of integer (list of possible number of block) or a p * Q matrix (for indicating block membership when its known)

sparsity

boolean to say whether the model should have a changing penalty OR float to run model with a single penalty value

zero_inflation

boolean to indicate if Y is zero-inflated and adjust fitted model as a consequence

control

a list-like structure for detailed control on parameters should be generated with normal_block_control() for collections of sparse models