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Rank-based regression strategy (R6 class)

Source: R/rfit.R
StrategyRfit.Rd

Rank-based regression strategy (R6 class)

Rank-based regression strategy (R6 class)

Value

An R6 class generator.

Details

Encapsulates everything needed to fit per-protein rank-based linear models via rfit and extract Wald contrasts. The fit is augmented (model frame, terms, rfit_prolfqua subclass) so it satisfies the same coef() / vcov() / terms() contract as lm.

Unlike StrategyLM, rfit takes no observation weights, so nr_children weighting is not supported by this backend.

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFirth, ContrastsFirthFacade, ContrastsFirthNestedFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, ContrastsLimpaFacade, ContrastsLimpaNestedFacade, ContrastsLmerNestedFacade, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsRLMFacade, ContrastsROPECA, ContrastsROPECANestedFacade, ContrastsRfitFacade, 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(), df.residual.rfit_prolfqua(), 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(), list_facades(), lookup_facade(), 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(), 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(), unregister_facade(), vcov.rfit_prolfqua()

Public fields

formula

model formula

model_name

name of model

report_columns

columns to report

is_mixed

always FALSE for rfit

anova_df

list with anova function and column names

Methods

Public methods

Method new()

Create a new StrategyRfit

Usage

StrategyRfit$new(
  modelstr,
  model_name = "rfit",
  report_columns = c("statistic", "p.value", "p.value.adjusted", "moderated.p.value",
    "moderated.p.value.adjusted")
)

Arguments

modelstr

model formula string

model_name

name of model

report_columns

columns to report

Method model_fun()

Fit rfit to one protein's data, augmenting the fit so the classic contrast path can introspect it.

Usage

StrategyRfit$model_fun(x, pb, get_formula = FALSE)

Arguments

x

data.frame for one protein

pb

optional progress bar

get_formula

if TRUE, return formula instead of fitting

Method isSingular()

Check if model is singular (NA coefficients or df < 2)

Usage

StrategyRfit$isSingular(model)

Arguments

model

fitted model

Method contrast_fun()

Compute contrasts from fitted model

Usage

StrategyRfit$contrast_fun(...)

Arguments

...

passed to compute_contrast

Method df_residual()

Get residual degrees of freedom

Usage

StrategyRfit$df_residual(model)

Arguments

model

fitted model

Method sigma()

Get the rank-based scale estimate

Usage

StrategyRfit$sigma(model)

Arguments

model

fitted model

Method clone()

The objects of this class are cloneable with this method.

Usage

StrategyRfit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

strat <- StrategyRfit$new("Intensity ~ condition", model_name = "parallel design")
strat$model_fun(get_formula = TRUE)
#> Intensity ~ condition
#> <environment: 0x55a0a7b663f8>