关于r：在ggplot2中绘制两个具有置信带的连续变量的相互作用

Plotting interaction of two continuous variables with confidence bands in ggplot2

我想为具有连续结果变量的两个连续预测变量生成具有简单斜率和95％置信带的交互图。

我们可以使用ggplot2中的diamonds数据来解决我的问题。我包括将因子变量的清晰度转换为均值中心的数字变量的语法，以便可以回答我的问题。

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# load package
library(ggplot2)
# rename data from ggplot2
d <- diamonds

# recode clarity from a factor variable into a numeric variable
levels(d$clarity)
library(plyr)
mapvalues(d$clarity, from = c("I1" , "SI2" ,"SI1" ,"VS2" ,"VS1" ,"VVS2" , "VVS1" ,"IF"),
to = c("1","2","3","4","5","6","7","8"))
d$clarity_n <- as.numeric(d$clarity)

我可以在下面的摘要输出中看到简单斜率的值。但是我不知道如何用置信带绘制它们。

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# create variables for simple effects
d$carat_MC <- d$carat - mean(d$carat, na.rm=T)
d$clarity_nMC <- d$clarity_n - mean(d$clarity_n, na.rm=T)
d$clarityPLUS_1sd <- d$clarity_nMC + sd(d$clarity_n, na.rm=T)
d$clarityMINUS_1sd <- d$clarity_nMC - sd(d$clarity_n, na.rm=T)

# create a small subset of 500
d <- d[sample(1:nrow(d), 500,replace=FALSE),]

# model the interaction and simple slopes
summary(lm(price~carat_MC*clarity_nMC, data = d))
# simple effect of increased carat for less clear diamonds
summary(lm(price~carat_MC*clarityPLUS_1sd, data = d))
# simple effect of increased carat for more clear diamonds
summary(lm(price~carat_MC*clarityMINUS_1sd, data = d))

我已经知道如何为因子变量和连续变量创建带有置信带的交互图。如果我对克拉的变量进行中位数拆分，您将看到非常像我最终想要得到的图：

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# create a new factor variable based on the median split
d$clarity_nMS[ d$clarity_nMC < median(d$clarity_nMC) ] <- -1
d$clarity_nMS[ d$clarity_nMC > median(d$clarity_nMC) ] <- 1
d$clarity_nMS <- as.factor( d$clarity_nMS )

# Begin plotting
ex <- ggplot(d, aes(carat_MC, price, color = clarity_nMS))

# jitter the scatter plot
ex <- ex + layer(geom ="point",
position = position_jitter(w = 0.1, h = 0.1))

# Add plot lines with confidence intervals.
ex <- ex + geom_smooth(method="lm", se=TRUE , fullrange=TRUE)
ex

对于我如何绘制上面的简单斜率，95％置信带以及可能的话，用两个连续的预测变量预测的简单斜率着色的数据点，我将不胜感激。