Encodes missingness as a binary response and fits the Firth logistic backend
used by the missingness model path in prolfquapp.
Arguments
- lfqdata
aggregated
LFQDataobject- modelstr
model formula string without the response variable (e.g.
"~ group_")
Value
a ModelFirth object
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_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()
Examples
istar <- sim_lfq_data_protein_config(
Nprot = 10, with_missing = TRUE, weight_missing = 0.5, seed = 3)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
mod <- build_model_glm_protein(lfqdata, "~ group_")
#> completing cases
#> Joining with `by = join_by(protein_Id)`
head(mod$get_coefficients())
#> # A tibble: 6 × 11
#> # Groups: protein_Id [2]
#> protein_Id factor Estimate se.coef. lower.0.95 upper.0.95 Chisq p
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7IZdVV~0841 (Interce… 2.20e+ 0 1.49 -0.0397 7.08 3.68e+ 0 0.0550
#> 2 7IZdVV~0841 group_B 5.61e-16 2.11 -5.33 5.33 0 1
#> 3 7IZdVV~0841 group_Ct… -2.20e+ 0 1.74 -7.28 0.755 2.03e+ 0 0.154
#> 4 AZPG26~2091 (Interce… 2.20e+ 0 1.49 -0.0397 7.08 3.68e+ 0 0.0550
#> 5 AZPG26~2091 group_B -1.52e-15 2.11 -5.33 5.33 8.88e-16 1.000
#> 6 AZPG26~2091 group_Ct… -1.07e-15 2.11 -5.33 5.33 8.88e-16 1.000
#> # ℹ 3 more variables: method <dbl>, isSingular <lgl>, nr_coef <int>