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Linear mixed-effects model strategy (R6 class)

Source: R/tidyMS_R6_Modelling.R
StrategyLmer.Rd

Linear mixed-effects model strategy (R6 class)

Linear mixed-effects model strategy (R6 class)

Details

Encapsulates everything needed to fit per-protein linear mixed-effects models via lmer 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, StrategyLogistf, 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 TRUE for lmer

anova_df

list with anova function and column names

Methods

Public methods

Method new()

Create a new StrategyLmer

Usage

StrategyLmer$new(
  modelstr,
  model_name = "Model",
  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 lmer to one protein's data

Usage

StrategyLmer$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

Usage

StrategyLmer$isSingular(model)

Arguments

model

fitted model

Method contrast_fun()

Compute contrasts from fitted model

Usage

StrategyLmer$contrast_fun(...)

Arguments

...

passed to my_contest

Method df_residual()

Get residual degrees of freedom

Usage

StrategyLmer$df_residual(model)

Arguments

model

fitted model

Method sigma()

Get residual standard error

Usage

StrategyLmer$sigma(model)

Arguments

model

fitted model

Method clone()

The objects of this class are cloneable with this method.

Usage

StrategyLmer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

istar <- prolfqua::sim_lfq_data_peptide_config(Nprot = 10, with_missing = FALSE)
#> creating sampleName from fileName column
#> completing cases
#> completing cases done
#> setup done
istar <- prolfqua::LFQData$new(istar$data, istar$config)
istar$data <- istar$data |> dplyr::group_by(protein_Id) |>
  dplyr::mutate(abundanceC = abundance - mean(abundance)) |> dplyr::ungroup()
strat <- StrategyLmer$new("abundanceC ~ group_ + (1|peptide_Id)",
  model_name = "random_example")
mod <- build_model(istar, strat)
#> Warning: There were 4 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `linear_model = purrr::map(data, model_strategy$model_fun, pb =
#>   pb)`.
#> ℹ In group 2: `protein_Id = "7cbcrd~5725"`.
#> Caused by warning in `value[[3L]]()`:
#> ! WARN :Error: grouping factors must have > 1 sampled level
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 3 remaining warnings.
#> Joining with `by = join_by(protein_Id)`
sum(mod$modelDF$exists_lmer)
#> [1] 6