mcca-package {mcca}

Diagnostic accuracy methods for classifiers

Description

Six common multi-category classification accuracy evaluation measures are included i.e., Correct Classification Percentage (CCP), Hypervolume Under Manifold (HUM), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), Polytomous Discrimination Index (PDI) and R-squared (RSQ). It allows users to fit many popular classification procedures, such as multinomial logistic regression, support vector machine, classification tree, and user computed risk values.

Details

Package:	mcca
Type:	Package
Version:	0.6
Date:	2019-08-05
License:	GPL

Functions

ccp	Calculate CCP Value
hum	Calculate HUM Value
plot.mcca.hum	Plot 3D ROC curve
idi	Calculate IDI Value
nri	Calculate NRI Value
pdi	Calculate PDI Value
rsq	Calculate RSQ Value
pm	Calculate Probability Matrix
ests	Estimated Information for Single Model Evaluation Value
estp	Estimated Information for Paired Model Evaluation Value

Installing and using

To install this package, make sure you are connected to the internet and issue the following command in the R prompt:

    install.packages("mcca")
  

To load the package in R:

    library(mcca)
  

Citation

Li J, Gao M, D'Agostino R. Evaluating classification accuracy for modern learning approaches. Statistics in Medicine. 2019;1-27. https://doi.org/10.1002/sim.8103

Author(s)

Ming Gao, Jialiang Li

Maintainer: Ming Gao <[e-mail]>

References

Li, J., Gao, M., D’Agostino, R. (2019). Evaluating Classification Accuracy for Modern Learning Approaches. Statistics in Medicine (Tutorials in Biostatistics). 38(13): 2477-2503.

Li, Ming G., D'Agostino. (2019). Evaluating Classification Accuracy for Modern Learning Approaches. Statistics in Medicine.

Li, J. and Fine, J. P. (2008): ROC analysis with multiple tests and multiple classes: methodology and applications in microarray studies. Biostatistics. 9 (3): 566-576.

Li, J., Chow, Y., Wong, W.K., and Wong, T.Y. (2014). Sorting Multiple Classes in Multi-dimensional ROC Analysis: Parametric and Nonparametric Approaches. Biomarkers. 19(1): 1-8.

Li, J., Jiang, B. and Fine, J. P. (2013). Multicategory reclassification statistics for assessing Improvements in diagnostic accuracy. Biostatistics. 14(2): 382-394.

Li, J., Jiang, B., and Fine, J. P. (2013). Letter to Editor: Response. Biostatistics. 14(4): 809-810.

Van Calster B, Vergouwe Y, Looman CWN, Van Belle V, Timmerman D and Steyerberg EW. Assessing the discriminative ability of risk models for more than two outcome categories. European Journal of Epidemiology 2012; 27: 761 C 770.

Li, J., Feng, Q., Fine, J.P., Pencina, M.J., Van Calster, B. (2018). Nonparametric estimation and inference for polytomous discrimination index. Statistical Methods in Medical Research. 27(10): 3092—3103.

See Also

CRAN packages HUM for HUM.

CRAN packages nnet, rpart, e1071, MASS employed in this package.

Examples

str(iris)
data <- iris[, 1:4]
label <- iris[, 5]
ccp(y = label, d = data, method = "multinom",maxit = 1000,MaxNWts = 2000,trace=FALSE)
ccp(y = label, d = data, method = "multinom")
ccp(y = label, d = data, method = "svm")
ccp(y = label, d = data, method = "svm",kernel="sigmoid",cost=4,scale=TRUE,coef0=0.5)
ccp(y = label, d = data, method = "tree")
p = as.numeric(label)
ccp(y = label, d = p, method = "label")
hum(y = label, d = data,method = "multinom")
hum(y = label, d = data,method = "svm")
hum(y = label, d = data,method = "svm",kernel="linear",cost=4,scale=TRUE)
hum(y = label, d = data, method = "tree")
ests(y = label, d = data,acc="hum",level=0.95,method = "multinom",trace=FALSE)

## $value
## [1] 0.9972

## $sd
## [1] 0.002051529

## $interval
## [1] 0.9935662 1.0000000