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Impute and refit singular/failed models

Source: R/tidyMS_build_model.R
impute_refit_singular.Rd

For proteins where the initial lm fit failed or produced NA coefficients, impute missing values with LOD, clamp, refit, and attach borrowed covariance.

Usage

impute_refit_singular(
  modelDF,
  model_strategy,
  lod,
  response,
  sample_template,
  borrow_method = c("sigma", "vcov"),
  df_method = c("observed", "borrowed")
)

Arguments

modelDF

tibble from model_analyse

model_strategy

strategy list from strategy_lm etc.

lod

numeric, limit of detection value

response

character, response column name in nested data

sample_template

data.frame with all sample/group combinations (columns matching the nested data minus the response). Used to complete proteins that are entirely missing in one or more groups.

borrow_method

"sigma" or "vcov"

df_method

"observed" uses max(n_observed - p, 1), "borrowed" uses median df from successful fits

Value

modified modelDF with imputed models replacing failed/singular ones

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(), compute_borrowed_variance(), contrasts_fisher_exact(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), 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()