对于线性回归和逻辑回归模型,可以使用predict()函数;对于支持向量机模型,可以使用predict()或predict.svr()函数。以下是几个示例: 预测结果: predictions1 <- predict(model1, newdata = test_data) predictions2 <- predict(model2, newdata = test_data) predictions3 <- predict(model3, newdata = test_...
用LASSO，adaptive LASSO预测通货膨胀时间序列|附代码数据
这是我们需要预测新数据的两件事情。例如,预测值并计算我们训练的数据的R 2值: y_predicted <- predict(fit, s = opt_lambda, newx = x) sst <- sum((y - mean(y))^2) sse <- sum((y_predicted - y)^2)# R squared rsq <-1- sse / sstrsq #> [1] 0.9318896 最优模型已经在训练数据...
[,y_name] dat_lasso_test=cbind(y,x) #testing set prediction y_predicted <- predict(cv.fit,s=cv.fit$lambda.min,newx=dat_lasso_test[,-1])) # Sum of Squares Total and Error sst <- sum((y - mean(y))^2) sse <- sum((y_predicted - y)^2) # R squared rsq <- round(1 ...
predict.glmnet(fit). For reference, here is the full signature of the predict.glmnet function/method (v3.0-2): predict(object, newx, s = NULL, type = c("link", "response", "coefficients", "nonzero", "class"), exact = FALSE, newoffset, ...) In the above, object is a fitt...
glmnet(as.matrix(x = train[, -which(names(train) %in% "Salesmodel$lambda.min lasso_predictions_valid <- predict(model,s = best_lambda,typ 浏览7提问于2019-03-18得票数 0 1回答 套索回归glmnet -关于输入数据的错误 、、 我尝试使用glmnet()拟合Lasso回归模型。由于我以前从未使用过Lasso回归,所以...
A function cva.glmnet to choose both the alpha and lambda parameters via cross-validation, following the approach described in the help page for cv.glmnet. Optionally does the cross-validation in parallel. Methods for plot, predict and coef for the above. You can install the development version...
:exclamation: This is a read-only mirror of the CRAN R package repository. glmnet — Lasso and Elastic-Net Regularized Generalized Linear Models. Homepage: https://glmnet.stanford.edu - gl
adalasso(x.in,y.in,crit="bic",penalty=factor) predict(adalasso, x.out) ## = 比较误差 = ## sqrt(mean((y.out-pred.ada)^2) 在这种情况下,adaLASSO产生了一个更精确的预测。一般来说,adaLASSO比简单的LASSO的预测效果更好。然而,这不是一个绝对的事实。我见过很多简单LASSO做得更好的案例。
predict.glmnetplot.glmnetprint.glmnetcoef.glmnet Author(s) Jerome Friedman, Trevor Hastie and Rob Tibshirani Maintainer: Trevor Hastie hastie@stanford.edu References Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent, https...