Fit a normal-block model with a variational or heuristic algorithm
Usage
normal_block(
data,
blocks,
sparsity = 0,
zero_inflation = FALSE,
control = NB_control()
)Arguments
- data
contains the matrix of responses (Y, n x p) and the design matrix (X, n x d)."
- blocks
either a 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
either TRUE to run the optimization for different sparsity penalty values OR float to run model with a single sparsity 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 NB_control().
Examples
## Normal Data
ex_data <- generate_normal_block_data(n=50, p=50, d=1, Q=3)
data <- NBData$new(ex_data$Y, ex_data$X)
my_normal_block <- normal_block(data, blocks = 1:6)
#> Fitting a diagonal normal-block model with unknown Q
#> number of blocks = 1
number of blocks = 2
number of blocks = 3
number of blocks = 4
number of blocks = 5
number of blocks = 6
#> DONE
if (FALSE) { # \dontrun{
my_normal_block$plot(c("deviance", "BIC", "ICL"))
Y_hat <- my_normal_block$get_best_model()$fitted
plot(data$Y, Y_hat, log = "xy"); abline(0,1)
} # }
## Normal Data with Zero Inflation
ex_data_zi <- generate_normal_block_data(n=50, p=50, d=1, Q=3, kappa = rep(0.5,50))
zidata <- NBData$new(ex_data_zi$Y, ex_data_zi$X)
my_normal_block <- normal_block(zidata, blocks = 1:6, zero_inflation = TRUE)
#> Fitting a diagonal normal-block model with unknown Q
#> number of blocks = 1
number of blocks = 2
number of blocks = 3
number of blocks = 4
number of blocks = 5
number of blocks = 6
#> DONE