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...
of 2 variables: ## $ death_rate: num 32.5 35.5 40.5 46 49 16 20.5 22.5 29 36 ... ## $ drug : chr "Drug_A" "Drug_A" "Drug_A" "Drug_A" ... 数据一共2列,第1列是死亡率,第2列是药物(3种)。 简单看下数据: boxplot(death_rate ~ drug, data = mydata) 进行Kruskal-Wallis ...
shape = NA) + ggtitle("G-CSF") + geom_jitter(width=0.2,col='gray45') + theme_classic() + scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) p3 p4<-dat02 %>% filter(cytokine == "IL1B") %>% mutate(new_value=qqnorm(value, plot.it = F)$x) %>% ...
Pearson product moment correlation: r is a measure of the linear dependence (correlation) between two variables X and Y. It has a value between +1 and −1 inclusive, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation co...
本文大部分内容来自一篇英文博客的翻译内容,博客原文链接:Plot Two Continuous Variables: Scatter Graph and Alternatives,但笔者也对一些参数进行了修改,使得图形更为美观。 加载所需的包并设置主题样式 library(ggplot2) library(ggpubr) library(ggpmisc)
ggdotplotstats dot plots/charts for distribution about labeled numeric variable ggscatterstats scatterplots for correlation between two variables ggcorrmat correlation matrices for correlations between multiple variables ggpiestats pie charts for categorical data ggbarstats bar charts for categorical data ...
hist(dt$weight)boxplot(dt$weight)qqnorm(dt$weight);qqline(dt$weight)#QQ图,添加趋势线 这里一直简单的叙述一下Q-Q图,学过简单的统计学理论就知道,这是一个右偏态,直方图也证明了这一点。 在接下来的统计描述中,就可以考虑使用中位数-四分位距对该变量进行...
install.packages("lavaan")install.packages("semPlot")install.packages("semTools") 接下来我们看下在此包中回归方程的几种表示形式: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 y~f1+f2+x1+x2 #回归方程~左边为因变量 f1=~y1+y2+y3 #潜变量表示形式 ...
3、rdplot 语法含义为 rdplot(y, x, c = 0, p = 4, nbins = NULL, binselect ="esmv", scale = NULL, kernel ="uni", weights = NULL, h = NULL, covs = NULL, covs_eval = 0, covs_drop = TRUE, support = NULL, subset = NULL, ...
# Compute the correlation matrix for these variables corrMat <- cor(data) # Generate the correlation ellipse plot corrplot(corrMat,method="ellipse") (8)构造和绘制rpart()模型 决策树容易观察和解释,是预测模型的一种常用方式。 # Load the rpart library ...