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Firth's logistic regression strategy (R6 class)

Source: R/logistf.R
StrategyLogistf.Rd

Firth's logistic regression strategy (R6 class)

Firth's logistic regression strategy (R6 class)

Details

Encapsulates everything needed to fit per-protein Firth's bias-reduced logistic regression via logistf and extract contrasts.

See also

Other modelling: AnovaExtractor, Contrasts, ContrastsDEqMSFacade, ContrastsFirth, ContrastsFirthFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLMMissingFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLmerFacade, ContrastsMissing, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsRLMFacade, ContrastsROPECA, ContrastsROPECAFacade, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, StrategyLM, StrategyLimma, StrategyLmer, StrategyRLM, build_contrast_analysis(), build_model(), build_model_glm_peptide(), build_model_glm_protein(), build_model_impute(), build_model_limma(), build_model_logistf(), compute_borrowed_variance(), 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(), model_summary(), moderated_p_deqms(), moderated_p_deqms_long(), moderated_p_limma(), moderated_p_limma_long(), my_contest(), my_contrast(), my_contrast_V2(), 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_logistf(), summary_ROPECA_median_p.scaled()

Public fields

formula

model formula

model_name

name of model

report_columns

columns to report

is_mixed

always FALSE for logistf

anova_df

list with anova function and column names

Methods

Public methods

Method new()

Create a new StrategyLogistf

Usage

StrategyLogistf$new(
  modelstr,
  model_name = "logistf",
  report_columns = c("statistic", "p.value", "p.value.adjusted", "moderated.p.value",
    "moderated.p.value.adjusted"),
  test = "Chisq"
)

Arguments

modelstr

model formula string

model_name

name of model

report_columns

columns to report

test

type of test statistic to use (e.g. "Chisq")

Method model_fun()

Fit logistf to one protein's data

Usage

StrategyLogistf$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

StrategyLogistf$isSingular(model)

Arguments

model

fitted model

Method contrast_fun()

Compute contrasts from fitted model

Usage

StrategyLogistf$contrast_fun(...)

Arguments

...

passed to my_contrast_V2

Method df_residual()

Get residual degrees of freedom

Usage

StrategyLogistf$df_residual(model)

Arguments

model

fitted model

Method sigma()

Get residual standard error (always 1 for logistic)

Usage

StrategyLogistf$sigma(model)

Arguments

model

fitted model

Method clone()

The objects of this class are cloneable with this method.

Usage

StrategyLogistf$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

strat <- StrategyLogistf$new("bin_resp ~ condition")
strat$model_fun(get_formula = TRUE)
#> bin_resp ~ condition
#> <environment: 0x561446a606a8>