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Compute borrowed variance from successful model fits

Source: R/tidyMS_build_model.R
compute_borrowed_variance.Rd

Compute borrowed variance from successful model fits

Usage

compute_borrowed_variance(modelDF, method = c("sigma", "vcov"))

Arguments

modelDF

tibble from model_analyse

method

"sigma" borrows scalar sigma and uses per-protein (X'X)^-1, "vcov" borrows element-wise median of full vcov matrices

Value

list with sigma, df, method, and optionally vcov

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsFirth, ContrastsFirthFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLmerFacade, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsRLMFacade, ContrastsROPECA, ContrastsROPECAFacade, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, StrategyLM, StrategyLimma, StrategyLmer, StrategyLogistf, StrategyRLM, build_contrast_analysis(), build_model(), build_model_glm_peptide(), build_model_glm_protein(), build_model_impute(), build_model_limma(), build_model_logistf(), contrasts_fisher_exact(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), impute_refit_singular(), isSingular_lm(), linfct_all_possible_contrasts(), linfct_factors_contrasts(), linfct_from_model(), linfct_matrix_contrasts(), merge_contrasts_results(), model_analyse(), model_summary(), moderated_p_deqms(), moderated_p_deqms_long(), moderated_p_limma(), moderated_p_limma_long(), my_contest(), my_contrast(), my_contrast_V2(), new_lm_imputed(), pivot_model_contrasts_2_Wide(), plot_lmer_peptide_predictions(), sim_build_models_lm(), sim_build_models_lmer(), sim_build_models_logistf(), sim_make_model_lm(), sim_make_model_lmer(), strategy_limma(), strategy_logistf(), summary_ROPECA_median_p.scaled()

Examples

mod <- sim_build_models_lm(model = "parallel3", weight_missing = 1)
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
#> Joining with `by = join_by(protein_Id)`

# Sigma method (default): returns median sigma and df from donors
borrowed_s <- prolfqua:::compute_borrowed_variance(
  mod$modelDF, method = "sigma")
stopifnot(borrowed_s$method == "sigma")
stopifnot(is.numeric(borrowed_s$sigma) && borrowed_s$sigma > 0)
stopifnot(is.numeric(borrowed_s$df) && borrowed_s$df > 0)

# Vcov method: element-wise median vcov from donors.
# Falls back to sigma if donor models have different coefficient counts.
mod_no_missing <- sim_build_models_lm(model = "parallel3",
  Nprot = 10, with_missing = FALSE)
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
#> Joining with `by = join_by(protein_Id)`
borrowed_v <- prolfqua:::compute_borrowed_variance(
  mod_no_missing$modelDF, method = "vcov")
stopifnot(borrowed_v$method == "vcov")
stopifnot(is.matrix(borrowed_v$vcov))