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Assumes a Gamma-Poisson model where the mean and variance are proportionally related.

Usage

# S3 method for class 'eSVD'
estimate_nuisance(
  input_obj,
  bool_covariates_as_library = F,
  bool_library_includes_interept = T,
  bool_use_log = F,
  min_val = 1e-04,
  verbose = 0,
  ...
)

Arguments

input_obj

eSVD object outputed from opt_esvd.eSVD. Specifically, the nuisance parameters will be estimated based on the fit in input_obj[[input_obj[["latest_Fit"]]]].

bool_covariates_as_library

Boolean to adjust the numerator in the posterior by the donor covariates, default is FALSE. This parameter is experimental, and we have not yet encountered a scenario where it is useful to be set to be TRUE.

bool_library_includes_interept

Boolean if the intercept term from the eSVD matrix factorization should be included in the calculation for the covariate-adjusted library size, default is TRUE.

bool_use_log

Boolean if the nuisance (i.e., over-dispersion) parameter should be estimated on the log scale, default is FALSE.

min_val

Minimum value of the nuisance parameter.

verbose

Integer.

...

Additional parameters.

Value

eSVD object with nuisance_vec appended to the list in input_obj[[input_obj[["latest_Fit"]]]].