Man pages for RomeroLab/pudms
Positive-Unlabeled Learning for the Analysis of Deep Mutational Scanning Datasets
| adjusted_roc_curve | Obtain an adjusted ROC curve from rocdata |
| auc | Return an adjusted (average) auc value from a fitted model at... |
| create_model_frame | Create a model frame list from a grouped data set |
| create_protein_dat | Create a data set for protein |
| create_seqmat | Create a model frame list from a grouped data set |
| cv_grouped_dat | Create (train,test) grouped data sets from a grouped data set |
| effective_n_seq | Number of effective sequences |
| filter_mut_less_than_thresh | Delete columns such that colSums <= nobs_thresh, and rows... |
| optimal_py1 | Search for optimal py1 values using roc_curves |
| pudms | DMS experiments analysis using PUlasso method |
| pval_pu | Obtain an asymptotic variance matrix of theta, standard... |
| roc.average | Average ROC curve |
| ROCdata | Create a data table (seqId, labeled, unlabeled, pr_test) to... |
| rocker | A rocker protein dataset |
| rocplot | Draw an adjusted ROC curve |
| sparse.model.matrix.parallel | Create a sparse model matrix in a parallel manner |
| v.pudms | test a PU fit on a test data set |
| wald_pvalue | Calculate a Wald statistic |
