Fit radEmu model with Firth penalty
Usage
emuFit_micro_penalized(
X,
Y,
B = NULL,
X_cup = NULL,
constraint_fn = NULL,
maxit = 500,
ml_maxit = 5,
tolerance = 0.001,
max_step = 5,
verbose = TRUE,
max_abs_B = 250,
use_legacy_augmentation = FALSE,
j_ref = NULL
)Arguments
- X
a p x J design matrix
- Y
an n x p matrix of nonnegative observations
- B
starting value of coefficient matrix (p x J)
- X_cup
design matrix for Y in long format. Defaults to NULL, in which case matrix is computed from X.
- constraint_fn
function g defining constraint on rows of B; g(B_k) = 0 for rows k = 1, ..., p of B.
- maxit
maximum number of coordinate descent cycles to perform before exiting optimization
- ml_maxit
numeric: maximum number of coordinate descent cycles to perform inside of maximum likelihood fits. Defaults to 5.
- tolerance
tolerance on improvement in log likelihood at which to exit optimization
- max_step
numeric: maximum sup-norm for proposed update steps
- verbose
logical: report information about progress of optimization? Default is TRUE.
- max_abs_B
numeric: maximum allowed value for elements of B (in absolute value). In most cases this is not needed as Firth penalty will prevent infinite estimates under separation. However, such a threshold may be helpful in very poorly conditioned problems (e.g., with many nearly collinear regressors). Default is 50.
- use_legacy_augmentation
logical: should an older (slower) implementation of data augmentation be used? Only used for testing - there is no advantage to using the older implementation in applied settings.
- j_ref
which column of B to set to zero as a convenience identifiability during optimization. Default is NULL, in which case this column is chosen based on characteristics of Y (i.e., j_ref chosen to maximize number of entries of Y_j_ref greater than zero).