Summarize modelling and error reporting
Arguments
- mod
model table see
build_model
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_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(),
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
D <- prolfqua::sim_lfq_data_peptide_config(Nprot = 20, weight_missing = 0.1)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
formula_rp <- strategy_lmer("abundance ~ group_ + (1 | peptide_Id) + (1 | sampleName)")
mod <- prolfqua::build_model(
LFQData$new(D$data, D$config), formula_rp)
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> Warning: There were 7 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `linear_model = purrr::map(data, model_strategy$model_fun, pb =
#> pb)`.
#> ℹ In group 1: `protein_Id = "0EfVhX~5954"`.
#> Caused by warning in `value[[3L]]()`:
#> ! WARN :Error: grouping factors must have > 1 sampled level
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 6 remaining warnings.
res <- model_summary(mod)
stopifnot(is.list(res))
stopifnot(all(c("exists", "isSingular") %in% names(res)))