Description Usage Arguments Value Examples
Coarse approximation linear function, randomized
1 2 | calf_subset(data, nMarkers, proportion = 0.8, targetVector, times = 1,
margin = NULL, optimize = "pval", verbose = FALSE)
|
data |
Matrix or data frame. First column must contain case/control dummy coded variable (if targetVector = "binary"). Otherwise, first column must contain real number vector corresponding to selection variable (if targetVector = "real"). All other columns contain relevant markers. |
nMarkers |
Maximum number of markers to include in creation of sum. |
proportion |
Numeric. A value (where 0 < proportion <= 1) indicating the proportion of cases and controls to use in analysis (if targetVector = "binary"). If targetVector = "real", this is just a proportion of the full sample. Used to evaluate robustness of solution. Defaults to 0.8. |
targetVector |
Indicate "binary" for target vector with two options (e.g., case/control). Indicate "real" for target vector with real numbers. |
times |
Numeric. Indicates the number of replications to run with randomization. |
margin |
Real number from 0 to 1. Indicates the amount a potential marker must improve the target criterion (Pearson correlation or p-value) in order to add the marker. |
optimize |
Criteria to optimize if targetVector = "binary." Indicate "pval" to optimize the p-value corresponding to the t-test distinguishing case and control. Indicate "auc" to optimize the AUC. |
verbose |
Logical. Indicate TRUE to print activity at each iteration to console. Defaults to FALSE. |
A data frame containing the chosen markers and their assigned weight (-1 or 1)
The AUC value for the classification. If multiple replications are requested, this will be a data.frame containing all AUCs across replications.
aucHist A histogram of the AUCs across replications.
1 | calf_subset(data = CaseControl, nMarkers = 6, targetVector = "binary", times = 5)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.