gof_criterion: Method 3: Goodness of fit criterion
In statdivlab/rre: Replicate Richness Estimator

Description Usage Arguments Details Examples

A regularization parameter λ is selected using a goodness of fit metric.

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gof_criterion(fct_list, lambda_vec = seq(0, 20, by = 2),
  starts = data.frame(alpha = c(0.01, 0.01), delta = c(0.01, 1e-04)),
  gof_method = "chi_sq", multiplier = 20, c_seq_len = 96, ...)

`fct_list`	A list of frequency count tables, assumed to be biological replicates.
`lambda_vec`	The values of the penalty parameter we consider in selecting λ.
`starts`	Starting values for `alpha` and `delta` in the MLE procedure.
`gof_method`	The only option currently supported is "chi_sq".
`multiplier`	The upper bound of the grid of candidate C values, stated in terms of a multiple of the maximum observed richess (c). For example if c is 50 and multiplier is 10, the method evaluates the likelihood in a C grid from 50 to 500.
`c_seq_len`	The number of points in the C grid search.

We generate a C estimate for each λ in lambda_vec. Using these estimates we use a χ-square goodness of fit statistic to evaluate the fit to the sample. The λ value with the best fit is selected_lambda, and the C estimate associated with that λ is ccc_hat. See paper for full details.