Create Firth's logistic regression strategy

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"),
  weights = NULL
)

strategy_rlm(
  modelstr,
  model_name = "Model",
  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
test: type of test statistic to use (e.g. "Chisq")
weights: optional character string naming a column in the data containing per-observation weights, passed to lm. Default NULL (unweighted).

Value

a StrategyLogistf object

a StrategyLmer object

a StrategyLM object

a StrategyRLM object

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(), 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(), 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_limpa(), summary_ROPECA_median_p.scaled()

Examples

tmp <- strategy_logistf("bin_resp ~ condition", model_name = "parallel design")
tmp$model_fun(get_formula = TRUE)
#> bin_resp ~ condition
#> <environment: 0x556e67499800>

istar <- prolfqua::sim_lfq_data_peptide_config(Nprot = 10, with_missing = TRUE,
  weight_missing = 0.5, seed = 3)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
istar$data <- encode_bin_resp(istar$data, istar$config)
#> completing cases
istar <- LFQData$new(istar$data, istar$config)
df <- istar$summarize_hierarchy()
df2 <- df[df[[ncol(df)]] > 1, ]
istar2 <- istar$get_subset(df2)
#> Joining with `by = join_by(protein_Id)`
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$data[[1]])
#> logistf::logistf(formula = self$formula, data = DFT, weights = Freq)
#> Model fitted by Penalized ML
#> Confidence intervals and p-values by Profile Likelihood 
#> 
#> Coefficients:
#>        (Intercept)            group_B         group_Ctrl peptide_IdFLq7LKTq 
#>       2.101899e+00       6.773389e-01      -6.663876e-01       3.440276e-16 
#> peptide_IdJYhOpuPH peptide_IdLiw5EMKP peptide_IdVcatZJTa peptide_IdjrLUqOjg 
#>      -1.068335e+00       3.432936e-16      -1.068335e+00       1.197563e+00 
#> peptide_Idq2jTaC1y 
#>      -1.445323e+00 
#> 
#> Likelihood ratio test=10.07111 on 8 df, p=0.2600707, n=84
#> 
modelFunction$model_fun(nestProtein$data[[4]])
#> logistf::logistf(formula = self$formula, data = DFT, weights = Freq)
#> Model fitted by Penalized ML
#> Confidence intervals and p-values by Profile Likelihood 
#> 
#> Coefficients:
#>        (Intercept)            group_B         group_Ctrl peptide_IdWcAw5ozd 
#>       8.375544e-03       8.360400e-11       9.365126e-01      -3.147690e-01 
#> peptide_IdgdnXrza3 peptide_IdxvlVt88v 
#>      -2.035802e-09      -6.303721e-01 
#> 
#> Likelihood ratio test=3.276542 on 5 df, p=0.657435, n=48
#> 
modelFunction <- strategy_lmer("abundanceC ~ group_ + (1|peptide_Id)",
  model_name = "random_example")
modelFunction$model_fun(get_formula = TRUE)
#> abundanceC ~ group_ + (1 | peptide_Id)
#> <environment: 0x556e63889920>
tmp <- strategy_lm("Intensity ~ condition", model_name = "parallel design")
tmp$model_fun(get_formula = TRUE)
#> Intensity ~ condition
#> <environment: 0x556e63817468>
tmp$weights
#> NULL
tmp <- strategy_rlm("Intensity ~ condition", model_name = "parallel design")
tmp$model_fun(get_formula = TRUE)
#> Intensity ~ condition
#> <environment: 0x556e63783d78>

Usage

Arguments

Value

See also

Examples