ContrastsTable | R Documentation |
holds results when contrasts are added.
holds results when contrasts are added.
prolfqua::ContrastsInterface
-> ContrastsTable
contrast_result
contrast results
modelName
model name
subject_Id
default protein_Id
new()
intitialize
ContrastsTable$new( contrastsdf, subject_Id = "protein_Id", modelName = "ContrastTable" )
contrastsdf
data.frame
subject_Id
default protein_Id
modelName
default ContrastTable
get_contrast_sides()
return sides of contrast
ContrastsTable$get_contrast_sides()
data.frame
get_linfct()
not implemented
ContrastsTable$get_linfct()
get_contrasts()
get contrasts
ContrastsTable$get_contrasts(all = FALSE)
all
should all columns be returned (default FALSE)
global
use a global linear function (determined by get_linfct)
get_Plotter()
get ContrastsPlotter
ContrastsTable$get_Plotter(FCthreshold = 1, FDRthreshold = 0.1)
FCthreshold
fold change threshold
FDRthreshold
fdr threshold
ContrastsPlotter
to_wide()
convert to wide format
ContrastsTable$to_wide(columns = c("p.value", "FDR", "statistic"))
columns
value column default beta.based.significance
data.frame
clone()
The objects of this class are cloneable with this method.
ContrastsTable$clone(deep = FALSE)
deep
Whether to make a deep clone.
summary_ROPECA_median_p.scaled
Other modelling:
Contrasts
,
ContrastsMissing
,
ContrastsModerated
,
ContrastsPlotter
,
ContrastsProDA
,
ContrastsROPECA
,
INTERNAL_FUNCTIONS_BY_FAMILY
,
LR_test()
,
Model
,
build_model()
,
contrasts_fisher_exact()
,
get_anova_df()
,
get_complete_model_fit()
,
get_p_values_pbeta()
,
isSingular_lm()
,
linfct_all_possible_contrasts()
,
linfct_factors_contrasts()
,
linfct_from_model()
,
linfct_matrix_contrasts()
,
merge_contrasts_results()
,
model_analyse()
,
model_summary()
,
moderated_p_limma()
,
moderated_p_limma_long()
,
my_contest()
,
my_contrast()
,
my_contrast_V1()
,
my_contrast_V2()
,
my_glht()
,
pivot_model_contrasts_2_Wide()
,
plot_lmer_peptide_predictions()
,
sim_build_models_lm()
,
sim_build_models_lmer()
,
sim_make_model_lm()
,
sim_make_model_lmer()
,
strategy_lmer()
,
summary_ROPECA_median_p.scaled()
bb <-prolfqua::sim_lfq_data_peptide_config()
configur <- bb$config$clone(deep=TRUE)
configur$table$hierarchyDepth <- 2
data <- bb$data
lfqdata <- LFQData$new(data, configur)
lfqdata$factors()
Contrasts <- c("aC" = "group_A - group_Ctrl",
"bC" = "group_A - group_Ctrl")
csi <- ContrastsMissing$new(lfqdata, contrasts = Contrasts)
ctr <- csi$get_contrasts()
csi$subject_Id
xcx <- ContrastsTable$new(ctr, subject_Id = csi$subject_Id, modelName = "TableTest")
xcx$get_contrasts()
xcx$get_Plotter()$volcano()
stopifnot(is.null(xcx$get_contrast_sides()))
stopifnot(is.null(xcx$get_linfct()))
stopifnot(ncol(xcx$to_wide()) == 10)
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