View source: R/tidyMS_R6_Modelling.R
moderated_p_limma_long | R Documentation |
Moderate p-value for long table
moderated_p_limma_long(
mm,
group_by_col = "lhs",
estimate = "estimate",
robust = FALSE
)
mm |
result of |
group_by_col |
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()
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)
mmm <- moderated_p_limma_long(factor_levelContrasts, group_by_col = "lhs")
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