Backward stepwise selection using the AIC in Cox proportional hazards regression modeling identified 6 variables that were the most associated with survival: age, tumor size, multiple lesions, nodal status, vascular invasion, and presence of cirrhosis of the underlying liver 我看生存分析列线图的文章这...
简单线性回归 simple linear regression x <- c(60,62,64,65,66,67,68,70,72,74) y <- c(63.6,65.2,66,65.5,66.9,67.1,67.4,68.3,70.1,70) dat <- data.frame(x=x,y=y) plot(dat) fit <- lm(y~x) summary(fit) ## ## Call: ## lm(formula = y ~ x) ## ## Residuals: ## Mi...
new.eg3.csv 603 · 百度网盘 new.eg5.csv 490 · 百度网盘 多元线性回归multiple linear regression ##例1:new.eg1 rm(list=ls()) setwd("/Users/sifan/R/datasets") dat <- read.csv("new.eg1.csv",header=T) dat ## x1 x2 x3 x4 y ## 1 5.68 1.90 4.53 8.2 11.2 ## 2 3.79 1.64 7....
# to a plot # Creating a plot plot(cars) # Calling abline() function abline(h=60,col="darkgreen") 输出: 在上面的例子中 abline() 函数在当前绘图的指定‘x’坐标处绘制一条水平线。 示例3:添加回归线 par(mgp=c(2,1,0),mar=c(3,3,1,1)) # Fit regression line require(stats) reg<-l...
subsets(leaps,statistic="cp",main="Cp Plot for All Subsets Regression") abline(1,1,lty=2,col="red") 12. 深层次分析 (1)交叉验证 交叉验证即将一定比例的数据挑选出来作为训练样本,另外的样本作为保留样本,先在训练样本上获取回归方程,然后在保留样本上做预测。 由于保留样本不涉及模型及参数的选择,...
In addition, you may want to have a look at some of the related articles on my homepage. A selection of articles about graphics in R is listed below.Draw Vertical Line to X-Axis of Class Date in ggplot2 Plot Add Regression Line to ggplot2 Plot in R Remove Vertical or Horizontal Grid...
R学习|一键单多因素回归分析及ggplot2可视化回归分析结果 :host { all: initial; -webkit-text-size-adjust: inherit; }.wx-root,body,page{--weui-BTN-HEIGHT:48;--weui-BTN-HEIGHT-MEDIUM:40;--weui-BTN-HEIGHT-SMALL:32}.wx-root,bod...
subsets(leaps,statistic="cp",main="Cp Plot for All subsets Regression") #基于Cp统计量的不同子模型的比较 abline(1,1,lty=2,col="red") 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 向前/向后回归
因此,在预测各地市局各分类前,采用 STL 方法先将月度数据分解为季节项与趋势项。然后,对趋势项数据采用线性回归(LinearRegression)拟合趋势,对季节项数据采用季节 ARIMA 模型,以年为周期进行拟合,以 mse 为标准挑选最优模型。最终得到各分类月度数据的预测结果,即趋势项预测与季节项预测之和。
from sklearn.linear_model import LogisticRegression import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from sklearn.preprocessing import scale # 数据标准化Z-score # 1、加载乳腺癌数据 data = datasets.load_breast_cancer() ...