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Build Firth logistic model for aggregated LFQData

Source: R/logistf.R
build_model_glm_protein.Rd

Encodes missingness as a binary response and fits the Firth logistic backend used by the missingness model path in prolfquapp.

Usage

build_model_glm_protein(lfqdata, modelstr)

Arguments

lfqdata

aggregated LFQData object

modelstr

model formula string without the response variable (e.g. "~ group_")

Value

a ModelFirth object

See also

Other modelling: Contrasts, ContrastsDEqMSFacade, ContrastsFirth, ContrastsFirthFacade, ContrastsLMFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLmerFacade, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsProDA, ContrastsROPECA, ContrastsROPECAFacade, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, build_contrast_analysis(), build_model(), build_model_glm_peptide(), build_model_limma(), build_model_logistf(), 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_V1(), my_contrast_V2(), my_glht(), 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()

Examples

istar <- sim_lfq_data_protein_config(Nprot = 10, with_missing = TRUE, weight_missing = 0.5, seed = 3)
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
mod <- build_model_glm_protein(lfqdata, "~ group_")
#> completing cases
#> Joining with `by = join_by(protein_Id)`
#> Joining with `by = join_by(protein_Id)`
head(mod$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>, nrcoef <int>