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交互作用效应(p for Interaction)在SCI文章中可以算是一个必杀技，几乎在高分的SCI中必出现，因为把人群分为亚组后再进行统计可以增强文章结果的可靠性，进行可视化后可以清晰的表明变量之间的关系。不仅如此，交互作用还可以使用来进行数据挖掘。既往咱们再文章《ggscitable包发布–一键生成1篇3.8分文章的亚组交互效应图》中已经介绍了分类变量如何做亚组交互，今天咱们继续来介绍ggscitable包如何做连续变量交互。
在文章Association between ultra-processed food and osteoporosis: a cross-sectional study based on the NHANES database（超加工食品与骨质疏松症之间的关系：基于NHANES数据库的横断面研究）中，作者为了介绍超加工食品和运动锻炼对骨质疏松的交互影响，生成了下面两个图，作者称交互热图和3D可视图
今天咱们通过ggscitable包来复现这两个图，先导入数据和R包
library(ggscitable)
bc<-read.csv("E:/r/test/rhc.csv",sep=',',header=TRUE)
这是个右心导管介入后的死亡数据，分类变量转成因子
bc$death<-ifelse(bc$death=="Yes",1,0)
bc$swang1<-ifelse(bc$swang1=="RHC",1,0)
bc$swang1<-as.factor(bc$swang1)
bc$sex<-as.factor(bc$sex)
Death是结局变量，其他的都是一些协变量，假设我想研究年龄和死亡的关系，想知道肌酐和年龄有没有交互作用。
肌酐和年龄都是连续变量，我们先来2D的
##2D
ggconinteraction(data = bc,x="age",y="death",Interaction="crea1",family = "glm",username=username,token=token)
修改X轴Y轴名字
###修改X轴Y轴名字
ggconinteraction(data = bc,x="age",y="death",Interaction="crea1",family = "glm",xlab = "年龄",ylab = "尿酸",username=username,token=token)
添加协变量
ggconinteraction(data = bc,x="age",y="death",Interaction="crea1",cov = c("sex"),family = "glm",username=username,token=token)
###修改颜色
ggconinteraction(data = bc,x="age",y="death",Interaction="crea1",cov = c("sex"),family = "glm",username=username,token=token,col = c("black", "white", "purple"))
默认的Y轴是log（P），如果你想表示为概率也可以转换，我们可以看到右边这个轴的概率明显不同了
###转换为概率
ggconinteraction(data = bc,x="age",y="death",Interaction="crea1",username=username,token=token,cov = c("sex"),family = "glm",trans = T)
下面绘制3D效果图，这个是你的电脑速度而定，我的电脑是有点慢，差不多要30秒
##3D
ggconinteraction(data = bc,x="age",y="death",Interaction="crea1",family = "glm",type = "3D",title=NULL,username=username,token=token)
下面是一些详细操作，修改标签、标题、颜色啥的
ggconinteraction(data = bc,x="age",y="death",Interaction="crea1",family = "glm",type = "3D",title="交互图",xlab = "年龄",ylab = "肌酐",username=username,token=token)
ggconinteraction(data = bc,x="age",y="death",Interaction="crea1",family = "glm",cov = c("sex"),type = "3D",title=NULL,trans = T,username=username,token=token)
ggconinteraction(data = bc,x="age",y="death",Interaction="crea1",family = "glm",cov = c("sex"),type = "3D",title=NULL,trans = T,col = "red",username=username,token=token)
还有一种可以转动的格式
ggconinteraction(data = bc,x="age",y="death",Interaction="crea1",family = "glm",cov = c("sex"),type = "rgl",title=NULL,trans = T,username=username,token=token)
接下来是线性回归，都是差不多的，我就不解释了，直接上代码
library(foreign)
be <- read.spss("E:/r/test/ozone.sav",use.value.labels=F, to.data.frame=T)ggconinteraction(data = be,x="ibh",y="ozon",Interaction="temp",family = "glm",username=username,token=token)
一些参数修改
ggconinteraction(data = be,x="ibh",y="ozon",Interaction="temp",family = "glm",username=username,token=token)
3D立体图
ggconinteraction(data = be,x="ibh",y="ozon",Interaction="temp",family = "glm",type = "3D",username=username,token=token)
一些细节修改
ggconinteraction(data = bc,x="age",y="death",Interaction="crea1",family = "glm",type = "3D",title="交互图",xlab = "高度",ylab = "温度",col = "red",username=username,token=token)
生存分析也是基本一样，就是多个时间参数
library(foreign)
bc <- read.spss("E:/r/Breast cancer survival agec.sav",use.value.labels=F, to.data.frame=T)
bc$histgrad<-as.factor(bc$histgrad)
bc$er<-as.factor(bc$er)
bc$pr<-as.factor(bc$pr)
bc$ln_yesno<-as.factor(bc$ln_yesno)
names(bc)
ggconinteraction(data = bc,x="age",y="status",Interaction="pathsize",family = "cox",time = "time",username=username,token=token)
3D图也基本一样
ggconinteraction(data = bc,x="age",y="status",Interaction="pathsize",family = "cox",time = "time",type = "3D",username=username,token=token)ggconinteraction(data = bc,x="age",y="status",Interaction="pathsize",family = "cox",time = "time",type = "3D",title="交互图",xlab = "年龄",ylab = "肿瘤大小",col = "red",username=username,token=token)
如果看不明白，下面还有视频操作
R语言基于ggscitable包复现一篇3.5分的文章的连续变量交互效应（交互作用）的可视化图