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build dataframe with models for testing

Source: R/simulate_LFQ_data.R
sim_build_models_lm.Rd

build dataframe with models for testing

Usage

sim_build_models_lm(
  model = c("parallel2", "parallel3", "factors", "interaction"),
  Nprot = 10,
  with_missing = TRUE,
  weight_missing = 1
)

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(), is_singular_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_to_wide(), plot_lmer_peptide_predictions(), 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

modi <- sim_build_models_lm(model = "interaction", weight_missing = 1)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
stopifnot(dim(modi$model_df) == c(10,9))
mod2 <- sim_build_models_lm(model = "parallel2", weight_missing = 1)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
mod2$model_df$linear_model[[1]]
#> 
#> Call:
#> lm(formula = self$formula, data = x)
#> 
#> Coefficients:
#> (Intercept)   TreatmentB  
#>      20.321        4.158  
#> 
mod3 <- sim_build_models_lm(model = "parallel3", weight_missing = 1)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
modf <- sim_build_models_lm(model = "factors", weight_missing = 1)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done