单击X轴系列值右侧的单元格选择按钮,用鼠标选择工作表中的单元格区域B3:B11,在文本框中输入逗号,然后再用鼠标选择工作表中的单元格区域D3:D11,再在文本框中输入逗号,接着再用鼠标选择工作表中的单元格区域...将Y轴系列值中的“={1}”删除,单击其右侧的单元格选择按钮,用鼠标选择工作表中的单元格区域C3:C1...
(px), prob = px, size = 1) cvob1 = cv.glmnet(x, y) plot(cvob1) title("Gaussian Family", line = 2.5) cvob1r = cv.glmnet(x, y, relax = TRUE) plot(cvob1r) frame() set.seed(1011) par(mfrow = c(2, 2), mar = c(4.5, 4.5, 4, 1)) cvob2 = cv.glmnet(x, ly,...
An option is provided that combines our bootstrap approach with glmnet for higher dimensional models. The plots and graphical user interface leverage state of the art web technologies to facilitate interaction with the results. The speed of implementation comes from the leaps package and cross-...
glmnet::cv.glmnet glmnet::glmnet KFAS::KFS KFAS::signal (inference) lfda::lfda lfda::klfda lfda::self maps::map MASS::isoMDS (inference) MASS::sammon (inference) raster::RasterBrick raster::RasterCommon raster::RasterLayer raster::RasterStack ROCR::performance sp::Line sp::Lines sp::Pol...
# mnl model using glmnet package mnl = caret::train(y ~.,data = train, method = "glmnet",trControl = fitControl) # random forest using ranger package, here tuned for speed (not accuracy!) rf = caret::train(y ~.,data = train, method = "ranger",trControl = fitControl, ...
需要的R包:DESeq2包(转录组差异分析),survival包(cox回归),survminer包(Kaplan-Meier Plot可视化),dplyr包(字符串处理),glmnet包(Lasso回归),ggplot2包(数据可视化),GGally包(绘制相关性矩阵图),rms包(计算VIF方差膨胀因子),survivalROC包(绘制time dependent ROC曲线),plotROC包(绘制ROC曲线)。
其实验证和构建差不多,只不过我们需要把构建时的系数拿出来用于验证数据集。为了让小伙伴们不再走小云走过的弯路,小云特意整理了一下!--完整代码供你ctrl V 代码: ibrary(glmnet)#载入所需R包y <- read.table(file = "Y.txt",header = T,sep = "\t",row.names = 1) 载入生存数据,行名为样本名,除...
需要的R包:DESeq2包(转录组差异分析),survival包(cox回归),survminer包(Kaplan-Meier Plot可视化),dplyr包(字符串处理),glmnet包(Lasso回归),ggplot2包(数据可视化),GGally包(绘制相关性矩阵图),rms包(计算VIF方差膨胀因子),survivalROC包(绘制time dependent ROC曲线),plotROC包(绘制ROC曲线)。
R语言lasso回归图奇怪,且交叉验证报错不断,考虑原因?dt <- dt_train_prex <- as.matrix(dt[, -1])y <- as.matrix(dt[,1])cvfit <- cv.glmnet(x,y,alpha=1,nfolds = 3,family='binomial')plot(cv.fit)print(cv.fit)fit <- glmnet(x,y,family = 'binomial',alpha = 1,nlambda = 1000)...
需要的R包:DESeq2包(转录组差异分析),survival包(cox回归),survminer包(Kaplan-Meier Plot可视化),dplyr包(字符串处理),glmnet包(Lasso回归),ggplot2包(数据可视化),GGally包(绘制相关性矩阵图),rms包(计算VIF方差膨胀因子),survivalROC包(绘制time dependent ROC曲线),plotROC包(绘制ROC曲线)。