strategy: Firth's Bias-Reduced Logistic Regression (logistf)
In wolski/prolfqua: Proteomics Label Free Quantification

strategy_logistf

R Documentation

Firth's Bias-Reduced Logistic Regression (logistf)

Description

The strategy contains functions to fit the model but also compute the contrasts etc.

Usage

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

strategy_lmer(
  modelstr,
  model_name = "Model",
  report_columns = c("statistic", "p.value", "p.value.adjusted", "moderated.p.value",
    "moderated.p.value.adjusted")
)

strategy_lm(
  modelstr,
  model_name = "Model",
  report_columns = c("statistic", "p.value", "p.value.adjusted", "moderated.p.value",
    "moderated.p.value.adjusted")
)

strategy_rlm(
  modelstr,
  model_name = "Model",
  report_columns = c("statistic", "p.value", "p.value.adjusted", "moderated.p.value",
    "moderated.p.value.adjusted")
)

strategy_glm(
  modelstr,
  model_name = "Model",
  test = "Chisq",
  family = stats::binomial,
  multiplier = 1,
  offset = 1,
  report_columns = c("statistic", "p.value", "p.value.adjusted", "moderated.p.value",
    "moderated.p.value.adjusted")
)

Arguments

`modelstr`	model formula
`model_name`	name of model
`report_columns`	columns to report
`family`	either binomial or quasibinomial
`multiplier`	for tuning default is 1.

Value

list with model function, contrast computation function etc.

Other modelling: Contrasts, ContrastsFirth, ContrastsMissing, ContrastsModerated, ContrastsPlotter, ContrastsProDA, ContrastsROPECA, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, build_model(), build_model_logistf(), contrasts_fisher_exact(), generate_contrasts(), generate_contrasts_for_factor(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), interaction_contrasts(), isSingular_lm(), level_specific_contrasts(), linfct_all_possible_contrasts(), linfct_factors_contrasts(), linfct_from_model(), linfct_matrix_contrasts(), main_effect_contrasts(), merge_contrasts_results(), model_analyse(), model_summary(), moderated_p_limma(), moderated_p_limma_long(), my_contest(), my_contrast(), my_contrast_V1(), my_contrast_V2(), my_glht(), pivot_model_contrasts_2_Wide(), plot_lmer_peptide_predictions(), process_factor(), sim_build_models_lm(), sim_build_models_lmer(), sim_build_models_logistf(), sim_make_model_lm(), sim_make_model_lmer(), summary_ROPECA_median_p.scaled()

Examples


tmp <- strategy_logistf("bin_resp ~ condition", model_name = "parallel design")
tmp$model_fun(get_formula = TRUE)
tmp$isSingular

istar <- prolfqua::sim_lfq_data_peptide_config(Nprot = 10, with_missing = TRUE, weight_missing = 0.5, seed = 3)
istar$data <- encode_bin_resp(istar$data, istar$config)
istar <- LFQData$new(istar$data, istar$config)
df <- istar$summarize_hierarchy()
df2 <- df[df[[ncol(df)]] > 1,  ]
istar2 <- istar$get_subset(df2)
istar2$data |>
dplyr::group_by(protein_Id) |>
 tidyr::nest() -> nestProtein
modelFunction <- strategy_logistf("bin_resp ~ group_ + peptide_Id", model_name = "random_example")
modelFunction$model_fun(nestProtein$data[[1]])
modelFunction$model_fun(nestProtein$data[[4]])


istar <- prolfqua::sim_lfq_data_peptide_config(Nprot = 10, with_missing = FALSE)
istar <- prolfqua::LFQData$new(istar$data,istar$config)
istar$data <- istar$data |> dplyr::group_by(protein_Id) |>
dplyr::mutate(abundanceC = abundance - mean(abundance)) |> dplyr::ungroup()
istar$factors()
modelFunction <- strategy_lmer("abundanceC ~ group_ + (1|peptide_Id) ", model_name = "random_example")
mod <- build_model(
 istar,
 modelFunction)
sum(mod$modelDF$exists_lmer)
sum(mod$modelDF$isSingular, na.rm=TRUE)



tmp <- strategy_lm("Intensity ~ condition", model_name = "parallel design")
tmp$model_fun(get_formula = TRUE)
tmp$isSingular
tmp <- strategy_rlm("Intensity ~ condition", model_name = "parallel design")
tmp$model_fun(get_formula = TRUE)
tmp$isSingular
tmp <- strategy_glm("Intensity ~ condition", model_name = "parallel design")
tmp$model_fun(get_formula = TRUE)
tmp$isSingular

wolski/prolfqua documentation built on June 8, 2025, 5:19 a.m.