cancerclass-package:
Development and validation of diagnostic tests from high-dimensional molecular data
Description
This package implements classification and validation methods for high-dimensional applications, such as gene expression data. The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol [1]. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements.
Details
|
Package: |
| cancerclass |
|
Type: |
| Package |
|
Version: |
| 1.5.1 |
|
Date: |
| 2013-09-04 |
References
[1] Michiels S, Koscielny S, Hill C (2005), Prediction of cancer outcome
with microarrays: a multiple random validation strategy, Lancet 365:488-492.
See Also
fit,
GOLUB1,
loo,
nvalidate,
nvalidation-class,
plot,
plot,nvalidation-method,
plot,prediction-method,
plot,predictor-method,
plot,validation-method,
plot3d,
plot3d,nvalidation-method,
plot3d,validation-method,
predict,
prediction-class,
predictor-class,
summary,
validate,
validation-class,
cancerclass-internal,
ilogit,
calc.roc,
calc.auc,
get.d,
get.d2,
get.prop,
get.ntrain,
prepare,
filter