The function simulates microarray data for two-group comparison with user supplied parameters such as number of biomarkers (genes or proteins), sample size, biological and experimental (technical) variation, replication, differential expression, and correlation between biomarkers.
Using interactive control panel (see rpanel) and 3D real-time rendering system (rgl), this package provides a user friendly GUI for estimating the minimum number of biomarkers (variables) needed to achieve a given level of accuracy for two-group classification problems based on microarray data.
Using interactive control panel (rpanel) and 3D real-time rendering system (rgl) , this package provides a user friendly GUI for estimating the minimum number of biomarkers (variables) needed to achieve a given level of accuracy for two-group classification problems based on microarray data.
This is a 7-dimensional array (database) of leave-one-out cross validation errors for Random Forest, Support Vector Machines, Linear Discriminant Analysis and k-Nearest Neighbour classifiers. The database is the basis for estimating the optimal number of biomarkers at a given error tolerance level using optimiseBiomarker function. See Details for more information.
Estimates misclassification errors (generalisation errors), sensitivity and specificity using cross-validation, bootstrap and 632plus bias corrected bootstrap methods based on Random Forest, Support Vector Machines, Linear Discriminant Analysis and k-Nearest Neighbour methods.
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