LOBOV_accuracies function

LOBOV_accuracies

Function created to visualize the accuracies in the current dataset compared to the accuracies in the Leave One Biobank Out Validation in Bizzarri et al.

LOBOV_accuracies(surrogates, bin_phenotypes, bin_pheno_available, acc_LOBOV)

Arguments

• surrogates: numeric data.frame containing the surrogate values by Bizzarri et al.
• bin_phenotypes: numeric data.frame with the binarized phenotypes output of binarize_all_pheno
• bin_pheno_available: vector of strings with the available phenotypes
• acc_LOBOV: accuracy of LOBOV calculated in Bizzarri et al.

Returns

Boxplot with the accuracies of the LOBOV

Details

Comparison of the AUCs of the surrogates in the updated dataset and the results of the Leave One Biobank Out Validation made in BBMRI-nl.

Examples

require(pROC)
require(plotly)
require(MiMIR)
require(foreach)
require(ggplot2)

#load the dataset
m <- synthetic_metabolic_dataset
p<- synthetic_phenotypic_dataset

#Calculating the binarized surrogates
b_p<-binarize_all_pheno(p)
#Apply a surrogate models and plot the ROC curve
sur<-calculate_surrogate_scores(m, p, MiMIR::PARAM_surrogates, bin_names=colnames(b_p))
p_avail<-colnames(b_p)[c(1:5)]
LOBOV_accuracies(sur$surrogates, b_p, p_avail, MiMIR::acc_LOBOV)

References

This function was made to vidualize the binarized variables calculated following the rules indicated in the article: Bizzarri,D. et al. (2022) 1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints. EBioMedicine, 75, 103764, doi:10.1016/j.ebiom.2021.103764