Metrics {NetDA}

Network-Based Discriminant Analysis Subject to Multi-Label Classes

Description

Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.

Usage

Metrics(yhat,Y_test)

Arguments

yhat

an m-dimensional “vector” of the predicted values determined by NetDA.

Y_test

an m-dimensional “vector” of the response from the testing data.

Details

This function aims to report the performance of classification results. The output includes confusion matrices and some commonly used criteria, such as precision, recall, F-score, and ARI.

Value

Confusion matrix

A confusion matrix based on predicted values and responses from the testing data

(PRE, REC, F-score)

Values of precision (PRE), recall (REC), and F-score

ARI

Values of the adjusted Rand index (ARI)

Author(s)

Chen, L.-P.

References

Chen, L.-P., Yi, G. Y., Zhang, Q., and He, W. (2019). Multiclass analysis and prediction with network structured covariates. Journal of Statistical Distributions and Applications, 6:6.

Hubert, L. and Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193-218.

See Also

NetDA

Examples

data(WineData)

Y = WineData[,1]     # the response
X = WineData[,2:14]  # the predictors

D1 = WineData[which(Y==1),]
D2 = WineData[which(Y==2),]
D3 = WineData[which(Y==3),]

Train = rbind(D1[1:45,], D2[1:45,],D3[1:45,])   # user-specific training data
Test = rbind(D1[45:dim(D1)[1],], D2[45:dim(D2)[1],],D3[45:dim(D3)[1],]) # user-specific testing data

X = Train[,2:14]
Y = Train[,1]
X_test = Test[,2:14]
Y_test = Test[,1]

NetDA(X,Y,method=1,X_test) -> NetLDA
yhat_lda = NetLDA$yhat
Metrics(yhat_lda,Y_test)

#############

NetDA(X,Y,method=2,X_test) -> NetQDA
yhat_qda = NetQDA$yhat
Metrics(yhat_qda,Y_test)