Likelihood ratio test

Likelihood ratio test

LR_test(
  modelProteinF,
  modelName,
  modelProteinF_Int,
  modelName_Int,
  subject_Id = "protein_Id",
  path = NULL
)

Arguments

modelProteinF

table with models (see build model)

modelName

name of model

modelProteinF_Int

reduced model

modelName_Int

name of reduced model

subject_Id

subject id typically Assession or protein_Id

path

default NULL, set to a directory if you need to write diagnostic plots.

See also

Other modelling: Contrasts, ContrastsMissing, ContrastsModerated, ContrastsPlotter, ContrastsProDA, ContrastsROPECA, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, Model, build_model(), contrasts_fisher_exact(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), isSingular_lm(), linfct_all_possible_contrasts(), linfct_factors_contrasts(), linfct_from_model(), linfct_matrix_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(), sim_build_models_lm(), sim_build_models_lmer(), sim_make_model_lm(), sim_make_model_lmer(), strategy_lmer(), summary_ROPECA_median_p.scaled()

Examples

data_2Factor <- prolfqua::sim_lfq_data_2Factor_config(
 Nprot = 200,
 with_missing = TRUE,
 weight_missing = 2)
#> creating sampleName from fileName column
#> completing cases

pMerged <- LFQData$new(data_2Factor$data, data_2Factor$config)

pMerged$config$table$get_response()
#> [1] "abundance"
pMerged$factors()
#> # A tibble: 16 × 4
#>    sample  sampleName Treatment Background
#>    <chr>   <chr>      <chr>     <chr>     
#>  1 A_V1    A_V1       A         X         
#>  2 A_V2    A_V2       A         X         
#>  3 A_V3    A_V3       A         X         
#>  4 A_V4    A_V4       A         X         
#>  5 B_V1    B_V1       B         X         
#>  6 B_V2    B_V2       B         X         
#>  7 B_V3    B_V3       B         X         
#>  8 B_V4    B_V4       B         X         
#>  9 C_V1    C_V1       B         Z         
#> 10 C_V2    C_V2       B         Z         
#> 11 C_V3    C_V3       B         Z         
#> 12 C_V4    C_V4       B         Z         
#> 13 Ctrl_V1 Ctrl_V1    A         Z         
#> 14 Ctrl_V2 Ctrl_V2    A         Z         
#> 15 Ctrl_V3 Ctrl_V3    A         Z         
#> 16 Ctrl_V4 Ctrl_V4    A         Z         

formula_condition_and_Batches <-
  prolfqua::strategy_lm("abundance ~ Treatment + Background")
modCB <- prolfqua::build_model(
  pMerged$data,
  formula_condition_and_Batches,
  subject_Id = pMerged$config$table$hierarchy_keys() )
#> Warning: There were 25 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 33: `protein_Id = "Br6sVH~3679"`.
#> Caused by warning in `value[[3L]]()`:
#> ! WARN :Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]): contrasts can be applied only to factors with 2 or more levels
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 24 remaining warnings.
#> Joining with `by = join_by(protein_Id)`

formula_condition <-
  prolfqua::strategy_lm("abundance ~ Treatment")
modC <- prolfqua::build_model(
  pMerged$data,
  formula_condition,
  subject_Id = pMerged$config$table$hierarchy_keys() )
#> Warning: There were 19 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 33: `protein_Id = "Br6sVH~3679"`.
#> Caused by warning in `value[[3L]]()`:
#> ! WARN :Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]): contrasts can be applied only to factors with 2 or more levels
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 18 remaining warnings.
#> Joining with `by = join_by(protein_Id)`

tmp <- LR_test(modCB$modelDF, "modCB", modC$modelDF, "modB")
hist(tmp$likelihood_ratio_test.pValue)