svmRFE(input, k = 5, halve.above = 100) #分割数据,分配随机数 nfold = 5 nrows = nrow(input) folds = rep(1:nfold, len=nrows)[sample(nrows)] folds = lapply(1:nfold, function(x) which(folds == x)) results = lapply(folds, svmRFE.wrap, input, k=5, halve.above=100) #特征选...
采用两种不同的机器学习算法(LASSO和SVM-RFE),最终确定7个标记基因。基于上述7个标记基因构建Logistic回归模型,ROC曲线显示,7个标记基因回归模型AUC = 0.748。同时分别对7个标记基因绘制了ROC曲线,所有基因的AUC均大于0.6。说明回归模型比单个标记基因具有更高的准确性和特异性。
svmRFE(input, k = 5, halve.above = 100) #分割数据,分配随机数 nfold = 5 nrows = nrow(input) folds = rep(1:nfold, len=nrows)[sample(nrows)] folds = lapply(1:nfold, function(x) which(folds == x)) results = lapply(folds, svmRFE.wrap, input, k=5, halve.above=100) #特征选...
svmRFE(input, k = 5, halve.above = 100) #分割数据,分配随机数 nfold = 5 nrows = nrow(input) folds = rep(1:nfold, len=nrows)[sample(nrows)] folds = lapply(1:nfold, function(x) which(folds == x)) results = lapply(folds, svmRFE.wrap, input, k=5, halve.above=100) #特征选...