scan_spline_lam: Scan spline regularization parameter

In incidental: Implements Empirical Bayes Incidence Curves

Description

This function holds degrees of freedom fixed and scans regularization parameter values.

Usage

scan_spline_lam(
  reported,
  delay_dist,
  lam_grid,
  method = "val",
  percent_thresh = 2,
  dof = 10,
  regularization_order = 2,
  reported_val = NULL,
  end_pad_size = 0,
  fisher_approx_cov = TRUE
)

Arguments

reported	An integer vector of reported cases.
delay_dist	A positive vector that sums to one, which describes the delay distribution.
lam_grid	A vector of regularization strengths to scan.
method	Metric to choose "best" dof: 'aic', 'bic', 'val'. If method='val', reported_val must be non NULL and match reported size.
percent_thresh	If using validation likelihood to select best, the largest (strongest) lambda that is within 'percent_thresh' of the highest validation lambda will be selected. Default is 2. Must be greater than 0.
dof	Degrees of freedom for spline basis.
regularization_order	An integer (typically 0, 1, 2), indicating differencing order for L2 regularization of spline parameters. Default is 2 for second derivative penalty.
reported_val	Validation time series of equal size to reported vector for use with 'val' method. Default is NULL.
end_pad_size	And integer number of steps the spline is defined beyond the final observation.
fisher_approx_cov	A flag to use either the Fisher Information (TRUE) or the Hessian (FALSE) to approx posterior covariance over parameters.

Value

List of outputs:

• best_lam = best lambda

• lam_resdf = data frame of fit statistics (lambda, dof, aic, bic, val_lls, train_lls)

incidental documentation built on Sept. 16, 2020, 5:07 p.m.