View source: R/tidyMS_R6Model.R
build_model | R Documentation |
Build protein models from data
build_model(
data,
model_strategy,
subject_Id = if ("LFQData" %in% class(data)) {
data$subject_Id()
} else {
"protein_Id"
},
modelName = model_strategy$model_name
)
data |
data - a data frame |
subject_Id |
grouping variable |
modelName |
model name |
modelFunction |
model function |
a object of class Model
model_analyse
, strategy_lmer
strategy_lm
Other modelling:
Contrasts
,
ContrastsMissing
,
ContrastsModerated
,
ContrastsPlotter
,
ContrastsProDA
,
ContrastsROPECA
,
ContrastsTable
,
INTERNAL_FUNCTIONS_BY_FAMILY
,
LR_test()
,
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()
D <- prolfqua::sim_lfq_data_peptide_config(Nprot = 20, weight_missing = 0.1)
D$data$abundance |> is.na() |> sum()
D <- prolfqua::sim_lfq_data_peptide_config(Nprot = 20, weight_missing = 0.1, seed =3)
D$data$abundance |> is.na() |> sum()
modelName <- "f_condtion_r_peptide"
formula_randomPeptide <-
strategy_lmer("abundance ~ group_ + (1 | peptide_Id) + (1 | sampleName)",
model_name = modelName)
mod <- prolfqua::build_model(
D$data,
formula_randomPeptide,
modelName = modelName,
subject_Id = D$config$table$hierarchy_keys_depth())
aovtable <- mod$get_anova()
mod <- prolfqua::build_model(
LFQData$new(D$data, D$config),
formula_randomPeptide,
modelName = modelName)
model_summary(mod)
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