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handles incomplete models

Source: R/tidyMS_contrasts.R
compute_contrast.Rd

only keeps non NA coefficients.

Usage

compute_contrast(m, linfct, confint = 0.95)

Arguments

m

linear model generated using lm

linfct

linear function

confint

confidence interval default 0.95

Details

When options(prolfqua.vectorize = TRUE) is set, dispatches to a vectorized implementation that uses matrix multiplication instead of a per-row loop. Set options(prolfqua.vectorize = FALSE) (the default) to use the original loop.

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_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(), merge_contrasts_results(), 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

m <- sim_make_model_lm( "factors")
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
linfct <- linfct_from_model(m)$linfct_factors
compute_contrast(m, linfct, confint = 0.95)
#>                     lhs    sigma df estimate std.error statistic      p.value
#> BackgroundX BackgroundX 1.557675 13 18.63959 0.5507212  33.84577 4.612815e-14
#> BackgroundZ BackgroundZ 1.557675 13 18.19440 0.5507212  33.03740 6.294815e-14
#> TreatmentA   TreatmentA 1.557675 13 18.75377 0.5507212  34.05311 4.264271e-14
#> TreatmentB   TreatmentB 1.557675 13 18.08021 0.5507212  32.83007 6.825487e-14
#>             conf.low conf.high
#> BackgroundX 17.44983  19.82935
#> BackgroundZ 17.00464  19.38416
#> TreatmentA  17.56401  19.94353
#> TreatmentB  16.89045  19.26998
compute_contrast(m, linfct, confint = 0.99)
#>                     lhs    sigma df estimate std.error statistic      p.value
#> BackgroundX BackgroundX 1.557675 13 18.63959 0.5507212  33.84577 4.612815e-14
#> BackgroundZ BackgroundZ 1.557675 13 18.19440 0.5507212  33.03740 6.294815e-14
#> TreatmentA   TreatmentA 1.557675 13 18.75377 0.5507212  34.05311 4.264271e-14
#> TreatmentB   TreatmentB 1.557675 13 18.08021 0.5507212  32.83007 6.825487e-14
#>             conf.low conf.high
#> BackgroundX 16.98066  20.29851
#> BackgroundZ 16.53547  19.85332
#> TreatmentA  17.09485  20.41269
#> TreatmentB  16.42129  19.73914