Moderate p-value for long table
moderated_p_limma_long(
mm,
group_by_col = "lhs",
estimate = "estimate",
robust = FALSE
)result of contrasts_linfct
colnames with contrast description - default 'lhs'
Other modelling:
Contrasts,
ContrastsMissing,
ContrastsModerated,
ContrastsPlotter,
ContrastsProDA,
ContrastsROPECA,
ContrastsTable,
INTERNAL_FUNCTIONS_BY_FAMILY,
LR_test(),
Model,
build_model(),
contrasts_fisher_exact(),
get_anova_df(),
get_complete_model_fit(),
get_p_values_pbeta(),
isSingular_lm(),
linfct_all_possible_contrasts(),
linfct_factors_contrasts(),
linfct_from_model(),
linfct_matrix_contrasts(),
merge_contrasts_results(),
model_analyse(),
model_summary(),
moderated_p_limma(),
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_make_model_lm(),
sim_make_model_lmer(),
strategy_lmer(),
summary_ROPECA_median_p.scaled()
mod <- sim_build_models_lm()
#> creating sampleName from fileName column
#> completing cases
#> Joining with `by = join_by(protein_Id)`
m <- get_complete_model_fit(mod$modelDF)
factor_contrasts <- linfct_factors_contrasts(m$linear_model[[1]])
factor_levelContrasts <- contrasts_linfct(
mod$modelDF,
factor_contrasts,
subject_Id = "protein_Id",
contrastfun = my_contrast_V2)
#> computing contrasts.
mmm <- moderated_p_limma_long(factor_levelContrasts, group_by_col = "lhs")