| Title: | 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. |
| Authors: | Li-Pang Chen |
| Maintainer: | Li-Pang Chen <[email protected]> |
| License: | GPL-2 |
| Version: | 0.2.0 |
| Built: | 2025-03-13 03:30:25 UTC |
| Source: | https://github.com/cran/NetDA |
Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.
There are two functions in this package: NetDA and Metrics. NetDA aims to construct network-based linear discriminant function and network-based quadratic discriminant function based on the training data, and then do classification for individuals in the testing data. Predicted values can be determined by NetDA. The function Metrics provides a confusion matrix and some commonly used criteria to assess the performance of classification and prediction.
Chen, L.-P.
Maintainer: Li-Pang Chen <[email protected]>
Chen, L.-P. (2022) Network-Based Discriminant Analysis for Multiclassification. Under revision.
Friedman, J., Hastie, T., and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432-441.
Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.
Metrics(yhat,Y_test)Metrics(yhat,Y_test)
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. |
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.
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) |
Chen, L.-P.
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.
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)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)
Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.
NetDA(X,Y, method,X_test)NetDA(X,Y, method,X_test)
X |
an (n,p) “matrix” of the predictors from the training data. |
Y |
an n-dimensional “vector” of the response from the training data. |
method |
a “scalar” to determine the classification method. “method = 1” represents network-based linear discriminant analysis (NetLDA); “method = 2” represents network-based quadratic discriminant analysis (NetQDA). |
X_test |
an (m,p) “matrix” of the predictors from the testing data. |
This function is used for the classification using discriminant analysis with network structures in predictors. NetLDA is formulated by linear discriminant function with the corresponding estimated precision matrix obtained by pooling all subjects in the training data; NetLDA is formulated by quadratic discriminant function with the estimated precision matrices determined by stratifying subjects from the associated classes.
yhat |
a vector of predicted responses obtained by NetLDA or NetQDA. |
Network |
the estimators of confusion matrices. |
Chen, L.-P.
Chen, L.-P. (2022) Network-Based Discriminant Analysis for Multiclassification. Under revision.
Friedman, J., Hastie, T., and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432-441.
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$yhat Net_lda = NetLDA$Network ############# NetDA(X,Y,method=2,X_test) -> NetQDA yhat_qda = NetQDA$yhat Net_qda = NetQDA$Networkdata(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$yhat Net_lda = NetLDA$Network ############# NetDA(X,Y,method=2,X_test) -> NetQDA yhat_qda = NetQDA$yhat Net_qda = NetQDA$Network
The dataset, available at https://archive.ics.uci.edu/ml/datasets/wine,
illustrates the usage of NetDA and Metrics functions.
The dataset contains measurements of 178 units where the response is classified as three classes
together with 13 relevant predictors.
data(WineData)data(WineData)
The dataset contains one response, 13 predictors, and 178 subjects.
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]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]