LOBOV_accuracies: LOBOV_accuracies

In DanieleBizzarri/MiMIR: Metabolomics-Based Models for Imputing Risk

LOBOV_accuracies

Description

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.

Usage

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.

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.

Value

Boxplot with the accuracies of the 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

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)

DanieleBizzarri/MiMIR documentation built on March 1, 2023, 11:02 p.m.