Optimize eSVD for eSVD objects

Usage

# S3 method for class 'eSVD'
opt_esvd(
  input_obj,
  fit_name = "fit_First",
  fit_previous = "fit_Init",
  l2pen = 0.1,
  max_iter = 100,
  offset_variables = NULL,
  tol = 1e-06,
  verbose = 0,
  ...
)

Arguments

input_obj: eSVD object output from initialize_esvd (or an earlier opt_esvd).
fit_name: String for the name of that will become the current fit when storing the results in input_obj.
fit_previous: String for the name of the previous fit that this function will grab the initialization values from.
l2pen: Small positive number for the amount of penalization for both the cells' and the genes' latent vectors as well as the coefficients.
max_iter: Positive integer for number of iterations.
offset_variables: A vector of strings depicting which column names in input_obj$covariates be treated as an offset during the optimization (i.e., their coefficients will not change throughout the optimization).
tol: Relative tolerance of the stopping rule: iteration stops once the objective changes by at most tol times max(1, |previous objective|).
verbose: Integer.
...: Additional parameters.

Value

eSVD object with added elements with name to whatever fit_name was set to.

Examples

set.seed(10)
sim <- generate_null(cell_per_person = 15, num_genes = 40,
                     num_individuals = 8)
esvd_obj <- initialize_esvd(dat = sim$obs_mat,
                            covariates = sim$covariates,
                            metadata_individual = sim$metadata_individual,
                            case_control_variable = "CC",
                            bool_intercept = TRUE,
                            k = 2,
                            lambda = 0.1)
esvd_obj <- reparameterization_esvd_covariates(input_obj = esvd_obj,
                                               fit_name = "fit_Init",
                                               omitted_variables = "Log_UMI")
esvd_obj <- opt_esvd(input_obj = esvd_obj,
                     max_iter = 5,
                     offset_variables = setdiff(colnames(esvd_obj$covariates), "CC"),
                     fit_name = "fit_First",
                     fit_previous = "fit_Init")
esvd_obj <- reparameterization_esvd_covariates(input_obj = esvd_obj,
                                               fit_name = "fit_First",
                                               omitted_variables = "Log_UMI")
esvd_obj$latest_Fit
#> [1] "fit_First"
esvd_obj$fit_First$loss
#> [1] -0.7940044 -0.8235692 -0.8328533 -0.8387476 -0.8419992