R6 class representing modelling result

R6 class representing modelling result

R6 class representing modelling result

See also

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(), 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(), is_singular_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_to_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(), strategy_logistf(), summary_ROPECA_median_p.scaled()

Super class

prolfqua::ModelInterface -> Model

Public fields

model_df

data.frame with modelling data and model.

model_name

name of model

subject_id

e.g. protein_Id

model_strategy

function to create the models

anova_df

function to compute anova

p.adjust

function to adjust p-values

Methods

Public methods

• Model$new()

• Model$get_coefficients()

• Model$get_anova()

• Model$coef_histogram()

• Model$coef_volcano()

• Model$coef_pairs()

• Model$anova_histogram()

• Model$write_anova_figures()

• Model$write_coef_figures()

• Model$clone()

---

Method new()

initialize

Usage

Model$new(
  model_df,
  model_strategy,
  model_name,
  subject_id = "protein_Id",
  p.adjust = prolfqua::adjust_p_values
)

Arguments

model_df

dataframe with modelling results

model_strategy

model_strategy see strategy_lmer

model_name

name of model

subject_id

subject column name

p.adjust

method to adjust p-values

---

Method get_coefficients()

return model coefficient table

Usage

Model$get_coefficients()

---

Method get_anova()

return anova table

Usage

Model$get_anova()

---

Method coef_histogram()

histogram of model coefficient

Usage

Model$coef_histogram()

---

Method coef_volcano()

volcano plot of non intercept coefficients

Usage

Model$coef_volcano()

---

Method coef_pairs()

pairs-plot of coefficients

Usage

Model$coef_pairs()

---

Method anova_histogram()

histogram of ANOVA results

Usage

Model$anova_histogram(what = c("p.value", "FDR"))

Arguments

what

show either "Pr..F." or "FDR.Pr..F."

---

Method write_anova_figures()

write figures related to ANOVA into pdf file

Usage

Model$write_anova_figures(path, width = 10, height = 10)

Arguments

path

folder name

width

figure width

height

figure height

---

Method write_coef_figures()

write figures related to Coefficients into pdf file

Usage

Model$write_coef_figures(path, width = 10, height = 10)

Arguments

path

folder name

width

figure width

height

figure height

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Model$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

istar <- sim_lfq_data_peptide_config(Nprot = 20)
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done
lfqdata <- LFQData$new(istar$data, istar$config)
lfqdata <- lfqdata$get_Transformer()$log2()$lfq
#> Column added : log2_abundance
model_name <- "f_condtion_r_peptide"
formula_randomPeptide <-
  strategy_lmer(paste0(lfqdata$response(), " ~ group_ + (1 | peptide_Id)"),
   model_name = model_name)
mod <- prolfqua::build_model(
 lfqdata,
 formula_randomPeptide,
 model_name = model_name)
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> boundary (singular) fit: see help('isSingular')
#> Warning: There were 7 warnings in `dplyr::mutate()`.
#> The first warning was:
#> ℹ In argument: `linear_model = purrr::map(data, model_strategy$model_fun, pb =
#>   pb)`.
#> ℹ In group 1: `protein_Id = "0EfVhX~5954"`.
#> Caused by warning in `value[[3L]]()`:
#> ! WARN :Error: grouping factors must have > 1 sampled level
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 6 remaining warnings.

mod$model_df
#> # A tibble: 20 × 9
#> # Groups:   protein_Id [20]
#>    protein_Id data     linear_model has_model_fit isSingular df.residual   sigma
#>    <chr>      <list>   <list>       <lgl>         <lgl>            <dbl>   <dbl>
#>  1 0EfVhX~59… <tibble> <chr [1]>    FALSE         NA                  NA NA     
#>  2 0m5WN4~14… <tibble> <lmrMdLmT>   TRUE          FALSE               15  0.120 
#>  3 7cbcrd~83… <tibble> <chr [1]>    FALSE         NA                  NA NA     
#>  4 9VUkAq~45… <tibble> <lmrMdLmT>   TRUE          FALSE              156  0.203 
#>  5 At886V~32… <tibble> <lmrMdLmT>   TRUE          FALSE               51  0.118 
#>  6 BEJI92~91… <tibble> <lmrMdLmT>   TRUE          TRUE                39  0.460 
#>  7 CGzoYe~28… <tibble> <chr [1]>    FALSE         NA                  NA NA     
#>  8 CtOJ9t~28… <tibble> <lmrMdLmT>   TRUE          FALSE               54  0.123 
#>  9 DoWup2~29… <tibble> <lmrMdLmT>   TRUE          FALSE               75  0.239 
#> 10 DuwH7n~34… <tibble> <lmrMdLmT>   TRUE          FALSE               28  0.215 
#> 11 Fl4JiV~75… <tibble> <chr [1]>    FALSE         NA                  NA NA     
#> 12 HC8K98~49… <tibble> <lmrMdLmT>   TRUE          TRUE                15  0.222 
#> 13 HvIpHG~40… <tibble> <lmrMdLmT>   TRUE          TRUE                18  0.158 
#> 14 I1Jk2Z~08… <tibble> <lmrMdLmT>   TRUE          FALSE               79  0.189 
#> 15 JV3Z7t~29… <tibble> <chr [1]>    FALSE         NA                  NA NA     
#> 16 JcKVfU~08… <tibble> <chr [1]>    FALSE         NA                  NA NA     
#> 17 JfvT8X~27… <tibble> <lmrMdLmT>   TRUE          FALSE              121  0.210 
#> 18 R2i6w7~02… <tibble> <lmrMdLmT>   TRUE          TRUE                15  0.124 
#> 19 SGIVBl~95… <tibble> <lmrMdLmT>   TRUE          FALSE               19  0.0997
#> 20 r2J0Eh~26… <tibble> <chr [1]>    FALSE         NA                  NA NA     
#> # ℹ 2 more variables: nr_coef <int>, nr_coef_not_NA <int>
aovtable  <- mod$get_anova()
mod$get_coefficients()
#> # A tibble: 39 × 9
#> # Groups:   protein_Id [13]
#>    protein_Id  factor     Estimate Std..Error     df t.value Pr...t.. isSingular
#>    <chr>       <chr>         <dbl>      <dbl>  <dbl>   <dbl>    <dbl> <lgl>     
#>  1 0m5WN4~1448 (Intercep…   4.16       0.0440   4.58  94.6   1.01e- 8 FALSE     
#>  2 0m5WN4~1448 group_B     -0.0614     0.0686  16.2   -0.896 3.83e- 1 FALSE     
#>  3 0m5WN4~1448 group_Ctrl   0.0815     0.0622  16.1    1.31  2.09e- 1 FALSE     
#>  4 9VUkAq~4562 (Intercep…   4.22       0.0449  27.8   94.0   2.49e-36 FALSE     
#>  5 9VUkAq~4562 group_B     -0.0902     0.0395 144.    -2.29  2.38e- 2 FALSE     
#>  6 9VUkAq~4562 group_Ctrl  -0.0699     0.0400 145.    -1.75  8.26e- 2 FALSE     
#>  7 At886V~3296 (Intercep…   4.12       0.0434   7.61  94.9   5.63e-13 FALSE     
#>  8 At886V~3296 group_B      0.0771     0.0393  49.1    1.96  5.55e- 2 FALSE     
#>  9 At886V~3296 group_Ctrl   0.122      0.0383  49.1    3.19  2.52e- 3 FALSE     
#> 10 BEJI92~9143 (Intercep…   4.51       0.123   41.0   36.7   5.60e-33 TRUE      
#> # ℹ 29 more rows
#> # ℹ 1 more variable: nr_coef <int>
mod$coef_histogram()
#> $plot

#> 
#> $name
#> [1] "Coef_Histogram_f_condtion_r_peptide.pdf"
#> 
mod$coef_volcano()
#> $plot

#> 
#> $name
#> [1] "Coef_volcano_plot_f_condtion_r_peptide.pdf"
#> 
mod$coef_pairs()
#> $plot
#> # A tibble: 13 × 4
#>    subject_id  `(Intercept)`  group_B group_Ctrl
#>    <chr>               <dbl>    <dbl>      <dbl>
#>  1 0m5WN4~1448          4.16 -0.0614      0.0815
#>  2 9VUkAq~4562          4.22 -0.0902     -0.0699
#>  3 At886V~3296          4.12  0.0771      0.122 
#>  4 BEJI92~9143          4.51  0.204       0.115 
#>  5 CtOJ9t~2837          4.84 -0.0838     -0.387 
#>  6 DoWup2~2934          4.43  0.316      -0.0374
#>  7 DuwH7n~3402          4.11 -0.00356     0.0990
#>  8 HC8K98~4958          3.89  0.322       0.0752
#>  9 HvIpHG~4015          4.11  0.661      -0.161 
#> 10 I1Jk2Z~0821          3.84  0.219       0.276 
#> 11 JfvT8X~2727          4.48 -0.0844     -0.194 
#> 12 R2i6w7~0288          4.65 -0.410      -0.396 
#> 13 SGIVBl~9558          5.12 -0.158      -0.344 
#> 
#> $name
#> [1] "Coef_Pairsplot_f_condtion_r_peptide.pdf"
#> 
mod$anova_histogram()
#> $plot

#> 
#> $name
#> [1] "Anova_p.values_f_condtion_r_peptide.pdf"
#>