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Merge contrast results coming from two different model.

Source: R/ContrastsInterface.R
merge_contrasts_results.Rd

Typically used with results of Contrasts and ContrastsMissing

Usage

merge_contrasts_results(prefer, add, model_name = "mergedModel")

Arguments

prefer

contrasts to use preferentially

add

contrasts to add from if missing in prefer

model_name

name of the merged model default "mergedModel"

Value

Contrast definitions or contrast results.

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFirth, ContrastsFirthFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, ContrastsLimpaFacade, ContrastsLmerFacade, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsRLMFacade, ContrastsROPECA, ContrastsROPECAFacade, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, StrategyLM, StrategyLimma, StrategyLimpa, StrategyLmer, StrategyLogistf, StrategyRLM, build_contrast_analysis(), build_model(), build_model_glm_peptide(), build_model_glm_protein(), build_model_impute(), build_model_limma(), build_model_limma_impute(), build_model_limma_voom(), build_model_limma_voom_impute(), build_model_limpa(), build_model_logistf(), compute_borrowed_variance(), compute_borrowed_variance_limma(), compute_contrast(), compute_lmer_contrast(), contrasts_fisher_exact(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), impute_refit_singular(), is_singular_lm(), linfct_all_possible_contrasts(), linfct_factors_contrasts(), linfct_from_model(), linfct_matrix_contrasts(), model_analyse(), model_summary(), moderated_p_deqms(), moderated_p_deqms_long(), moderated_p_limma(), moderated_p_limma_long(), new_lm_imputed(), pivot_model_contrasts_to_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_limpa(), strategy_logistf(), summary_ROPECA_median_p.scaled()

Examples

prefer_df <- data.frame(
  protein_Id = c("P1", "P2"),
  contrast = "A_vs_B",
  modelName = "prefer",
  diff = c(1, 2),
  p.value = c(0.01, 0.2),
  FDR = c(0.02, 0.2),
  statistic = c(3, NA)
)
add_df <- data.frame(
  protein_Id = c("P1", "P2"),
  contrast = "A_vs_B",
  modelName = "add",
  diff = c(1.1, 2.1),
  p.value = c(0.02, 0.03),
  FDR = c(0.03, 0.04),
  statistic = c(2.8, 2.5)
)
prefer <- ContrastsTable$new(prefer_df, subject_id = "protein_Id", model_name = "prefer")
add <- ContrastsTable$new(add_df, subject_id = "protein_Id", model_name = "add")
merged <- merge_contrasts_results(prefer, add)
#> Joining with `by = join_by(protein_Id, contrast)`
#> Joining with `by = join_by(protein_Id, contrast)`
merged$merged$get_contrasts()
#>   protein_Id contrast modelName diff p.value  FDR statistic
#> 1         P1   A_vs_B    prefer  1.0    0.01 0.02       3.0
#> 2         P2   A_vs_B       add  2.1    0.03 0.04       2.5