Firth logistic missingness contrast analysis facade
Source:R/ContrastsFacades.R
ContrastsFirthFacade.RdFirth logistic missingness contrast analysis facade
Firth logistic missingness contrast analysis facade
Details
Encapsulates the pipeline: encode missingness ->
build_model_glm_protein or
build_model_glm_peptide -> ContrastsFirth.
The input may be aggregated protein-level data or nested peptide-level data.
The correct builder is chosen from the LFQData hierarchy automatically.
Supports options(prolfqua.vectorize = TRUE) for faster contrast
computation. See build_contrast_analysis for details.
See also
Other modelling:
AnovaExtractor,
Contrasts,
ContrastsDEqMSFacade,
ContrastsDEqMSVoomFacade,
ContrastsFirth,
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(),
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(),
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_limpa(),
strategy_logistf(),
summary_ROPECA_median_p.scaled()
Public fields
modelModelFirth object
contrastContrastsFirth object
.lfqdatastored reference to input LFQData
.contrast_namesnames of the requested contrasts
Methods
Examples
istar <- sim_lfq_data_protein_config(Nprot = 20, weight_missing = 0.5)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
contrasts <- c("A_vs_Ctrl" = "group_A - group_Ctrl")
fa <- ContrastsFirthFacade$new(lfqdata, "~ group_", contrasts)
#> completing cases
#> Joining with `by = join_by(protein_Id)`
head(fa$get_contrasts())
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct_firth
#> Joining with `by = join_by(protein_Id, contrast)`
#> # A tibble: 6 × 14
#> # Groups: contrast [1]
#> facade modelName protein_Id contrast sigma df diff FDR std.error
#> <chr> <chr> <chr> <chr> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 firth WaldTestFirth 0EfVhX~59… A_vs_Ct… 1 9 1.07e-15 1 2.11
#> 2 firth WaldTestFirth 0m5WN4~14… A_vs_Ct… 1 9 8.47e- 1 0.978 1.32
#> 3 firth WaldTestFirth 7cbcrd~83… A_vs_Ct… 1 9 1.07e-15 1 2.11
#> 4 firth WaldTestFirth 9VUkAq~45… A_vs_Ct… 1 9 -1.35e+ 0 0.978 1.78
#> 5 firth WaldTestFirth At886V~32… A_vs_Ct… 1 9 -8.47e- 1 0.978 1.32
#> 6 firth WaldTestFirth BEJI92~91… A_vs_Ct… 1 9 -1.35e+ 0 0.978 1.78
#> # ℹ 5 more variables: statistic <dbl>, p.value <dbl>, conf.low <dbl>,
#> # conf.high <dbl>, avgAbd <dbl>
fa$to_wide()
#> # A tibble: 20 × 5
#> protein_Id diff.A_vs_Ctrl p.value.A_vs_Ctrl FDR.A_vs_Ctrl statistic.A_vs_Ctrl
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 0EfVhX~59… 1.07e-15 1 1 5.08e-16
#> 2 0m5WN4~14… 8.47e- 1 0.538 0.978 6.40e- 1
#> 3 7cbcrd~83… 1.07e-15 1 1 5.08e-16
#> 4 9VUkAq~45… -1.35e+ 0 0.468 0.978 -7.58e- 1
#> 5 At886V~32… -8.47e- 1 0.538 0.978 -6.40e- 1
#> 6 BEJI92~91… -1.35e+ 0 0.468 0.978 -7.58e- 1
#> 7 CGzoYe~28… -4.13e-16 1 1 -1.96e-16
#> 8 CtOJ9t~28… 1.35e+ 0 0.468 0.978 7.58e- 1
#> 9 DoWup2~29… 2.20e+ 0 0.238 0.978 1.26e+ 0
#> 10 DuwH7n~34… 8.47e- 1 0.538 0.978 6.40e- 1
#> 11 Fl4JiV~75… -2.85e-17 1 1 -2.26e-17
#> 12 HC8K98~49… 8.47e- 1 0.538 0.978 6.40e- 1
#> 13 HvIpHG~40… 1.07e-15 1 1 5.08e-16
#> 14 I1Jk2Z~08… -8.47e- 1 0.538 0.978 -6.40e- 1
#> 15 JV3Z7t~29… 1.07e-15 1 1 5.08e-16
#> 16 JcKVfU~08… -1.35e+ 0 0.468 0.978 -7.58e- 1
#> 17 JfvT8X~27… -2.20e+ 0 0.238 0.978 -1.26e+ 0
#> 18 R2i6w7~02… 6.65e-17 1 1 5.26e-17
#> 19 SGIVBl~95… 1.07e-15 1 1 5.08e-16
#> 20 r2J0Eh~26… -4.13e-16 1 1 -1.96e-16