Estimate contrasts using Wald Test

Estimate contrasts using Wald Test

Estimate contrasts using Wald Test

Value

An R6 class generator.

Details

The per-protein contrast computation uses compute_contrast and linfct_matrix_contrasts internally.

See also

Other modelling: AnovaExtractor, ContrastsDEqMSFacade, ContrastsDEqMSVoomFacade, ContrastsFacadeBase, ContrastsFirth, ContrastsFirthFacade, ContrastsFirthNestedFacade, ContrastsLMFacade, ContrastsLMImputeFacade, ContrastsLimma, ContrastsLimmaFacade, ContrastsLimmaImputeFacade, ContrastsLimmaVoomFacade, ContrastsLimmaVoomImputeFacade, ContrastsLimpaFacade, ContrastsLimpaNestedFacade, ContrastsLmerNestedFacade, ContrastsModerated, ContrastsModeratedDEqMS, ContrastsPlotter, ContrastsRLMFacade, ContrastsROPECA, ContrastsROPECANestedFacade, ContrastsRfitFacade, ContrastsRfitImputeFacade, ContrastsTable, INTERNAL_FUNCTIONS_BY_FAMILY, LR_test(), Model, ModelFirth, ModelLimma, StrategyLM, StrategyLimma, StrategyLimpa, StrategyLmer, StrategyLogistf, StrategyRLM, StrategyRfit, 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(), df.residual.rfit_prolfqua(), get_anova_df(), get_complete_model_fit(), get_p_values_pbeta(), group_label(), impute_from_model(), impute_refit_singular(), linfct_from_model(), linfct_matrix_contrasts(), list_facades(), lookup_facade(), merge_contrasts_results(), model_analyse(), model_summary(), moderated_p_deqms(), moderated_p_deqms_long(), moderated_p_limma(), new_imputed_model(), pivot_model_contrasts_to_wide(), register_facade(), sigma.rfit_prolfqua(), 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(), vcov.rfit_prolfqua()

Super class

prolfqua::ContrastsInterface -> Contrast

Public fields

models

Model

contrasts

character with contrasts

contrastfun

function to compute contrasts

model_name

model name

subject_id

name of column containing e.g., protein Id's

p.adjust

function to adjust p-values (default prolfqua::adjust_p_values)

contrast_result

data frame containing results of contrast computation

global

use a global linear function (determined by get_linfct)

Methods

Public methods

• Contrasts$new()

• Contrasts$get_contrast_sides()

• Contrasts$get_linfct()

• Contrasts$get_contrasts()

• Contrasts$clone()

Inherited methods

• prolfqua::ContrastsInterface$column_description()
• prolfqua::ContrastsInterface$contrast_summary_table()
• prolfqua::ContrastsInterface$extra_artifacts()
• prolfqua::ContrastsInterface$filter_significant()
• prolfqua::ContrastsInterface$get_Plotter()
• prolfqua::ContrastsInterface$get_config()
• prolfqua::ContrastsInterface$get_missing()
• prolfqua::ContrastsInterface$get_ora()
• prolfqua::ContrastsInterface$get_rank()
• prolfqua::ContrastsInterface$to_wide()

---

Method new()

initialize create Contrast

Usage

Contrasts$new(
  model,
  contrasts,
  p.adjust = prolfqua::adjust_p_values,
  global = FALSE,
  model_name = "WaldTest"
)

Arguments

model

a dataframe with a structure similar to that generated by build_model

contrasts

a character vector with contrast specification

p.adjust

function to adjust the p-values

global

development/internal argument (if FALSE determine linfct for each model.)

model_name

name of contrast method, default WaldTest

---

Method get_contrast_sides()

get both sides of contrasts

Usage

Contrasts$get_contrast_sides()

---

Method get_linfct()

get linear functions from contrasts

Usage

Contrasts$get_linfct(global = TRUE, avg = TRUE)

Arguments

global

logical TRUE - get the a linear functions for all models, FALSE - linear function for each model

avg

logical TRUE - get also linfct for averages

---

Method get_contrasts()

get table with contrast estimates

Usage

Contrasts$get_contrasts(all = FALSE)

Arguments

all

should all columns be returned (default FALSE)

Returns

data.frame with contrasts

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Contrasts$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Fitting mixed effects model to peptide data
istar <- prolfqua::sim_lfq_data_peptide_config()
#> creating sampleName from file_name column
#> completing cases
#> completing cases done
#> setup done

modelFunction <-
  strategy_lmer("abundance  ~ group_ + (1 | peptide_Id) + (1 | sample)")

config <- istar$config
config$hierarchy_keys_depth()
#> [1] "protein_Id"

mod <- build_model(
  istar$data,
  modelFunction,
  subject_id = config$hierarchy_keys_depth()
)
#> boundary (singular) fit: see help('isSingular')
#> 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 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:
#> ! grouping factors must have > 1 sampled level
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 3 remaining warnings.

ref_lfc <- data.frame(
  `(Intercept)` = c(0, 0, 0),
  group_B = c(0, 1, 0),
  group_Ctrl = c(-1, -1, 1),
  row.names = c("groupA_vs_Ctrl", "dil.e_vs_b", "dil.ctrl_vs_b")
)
prolfqua::model_summary(mod)
#> $exists
#> 
#> FALSE  TRUE 
#>     4     6 
#> 
#> $isSingular
#> 
#> FALSE  TRUE 
#>     1     5 
#> 
Contr <- c(
  "groupA_vs_Ctrl" = "group_A - group_Ctrl",
  "dil.e_vs_b" = "group_B - group_Ctrl",
  "dil.ctrl_vs_b" = "group_Ctrl - group_A"
)
contrastX <- prolfqua::Contrasts$new(mod, Contr)
y <- contrastX$get_linfct(avg = FALSE)
stopifnot(all(ref_lfc == y))
t <- contrastX$get_linfct(global = FALSE)

x <- contrastX$get_contrasts()
#> determine linear functions:
#> get_contrasts -> contrasts_linfct
#> contrasts_linfct
#> Joining with `by = join_by(protein_Id, contrast)`
stopifnot(all(x$p.value < 1 & x$p.value > 0))
stopifnot(all(x$avgAbd > 0))
stopifnot(all(x$FDR > 0 & x$FDR < 1))

x <- contrastX$get_contrast_sides()
xd <- contrastX$column_description()
modelFunction <-
  strategy_lm("abundance  ~ group_")
mod <- build_model(
  istar$data,
  modelFunction,
  subject_id = config$hierarchy_keys_depth()
)
contrastX <- prolfqua::Contrasts$new(mod, Contr)
y <- contrastX$get_linfct(avg = FALSE)
stopifnot(all(ref_lfc == y))