Linear model strategy (R6 class)

Linear model strategy (R6 class)

Linear model strategy (R6 class)

Value

An R6 class generator.

Details

Encapsulates everything needed to fit per-protein linear models and extract contrasts: the formula, model fitting function, singularity check, contrast computation, ANOVA, and residual statistics.

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFacadeBase, ContrastsFirth, ContrastsFirthFacade, ContrastsFirthNestedFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, ContrastsLimpaFacade, ContrastsLimpaNestedFacade, ContrastsLmerNestedFacade, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsRLMFacade, ContrastsROPECA, ContrastsROPECANestedFacade, ContrastsRfitFacade, ContrastsRfitImputeFacade, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, StrategyLimma, StrategyLimpa, StrategyLmer, StrategyLogistf, StrategyRLM, StrategyRfit, 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(), df.residual.rfit_prolfqua(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), impute_from_model(), impute_refit_singular(), linfct_from_model(), linfct_matrix_contrasts(), list_facades(), lookup_facade(), merge_contrasts_results(), model_analyse(), model_summary(), moderated_p_deqms(), moderated_p_deqms_long(), moderated_p_limma(), new_imputed_model(), pivot_model_contrasts_to_wide(), register_facade(), sigma.rfit_prolfqua(), 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(), vcov.rfit_prolfqua()

Super class

prolfqua::StrategyBase -> StrategyLM

Public fields

weights

optional character string naming a column in the data containing per-observation weights, passed to lm.

Methods

Public methods

• StrategyLM$new()

• StrategyLM$clone()

Inherited methods

• prolfqua::StrategyBase$contrast_fun()
• prolfqua::StrategyBase$df_residual()
• prolfqua::StrategyBase$isSingular()
• prolfqua::StrategyBase$model_fun()
• prolfqua::StrategyBase$sigma()

---

Method new()

Create a new StrategyLM

Usage

StrategyLM$new(modelstr, model_name = "Model", weights = NULL)

Arguments

modelstr

model formula string

model_name

name of model

weights

optional character string naming a column in the data containing per-observation weights

---

Method clone()

The objects of this class are cloneable with this method.

Usage

StrategyLM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

strat <- StrategyLM$new("Intensity ~ condition", model_name = "parallel design")
strat$formula
#> Intensity ~ condition
#> <environment: 0x557451eb0888>
strat$weights
#> NULL