Question

我想将回归线添加到我的相关散点图中。不幸的是，这实际上不适用于plot_ly()。我已经在该论坛的其他帖子中尝试过一些解决方案，但这是行不通的。

我的数据框如下所示（只是其中的一部分）：

我的绘图代码和实际绘图输出如下：

CorrelationPlot <- plot_ly(data = df.dataCorrelation, x = ~df.dataCorrelation$prod1, 
                           y = ~df.dataCorrelation$prod2, type = 'scatter', mode = 'markers',
                           marker = list(size = 7, color = "#FF9999", line = list(color = "#CC0000", width = 2))) %>%
                    layout(title = "<b> Correlation Scatter Plot", xaxis = list(title = product1), 
                           yaxis = list(title = product2), showlegend = FALSE)

我想要的是这样的东西：

我用ggscatter()函数产生的

：

library(ggpubr)
  ggscatter(df.dataCorrelation, x = "prod1", y = "prod2", color = "#CC0000", shape = 21, size = 2,
            add = "reg.line", add.params = list(color = "#CC0000", size = 2), conf.int = TRUE, 
            cor.coef = TRUE, cor.method = "pearson", xlab = product1, ylab = product2)

我如何用plot_ly()得到回归线？

代码编辑：

CorrelationPlot <- plot_ly(data = df.dataCorrelation, x = ~df.dataCorrelation$prod1, 
                           y = ~df.dataCorrelation$prod2, type = 'scatter', mode = 'markers',
                           marker = list(size = 7, color = "#FF9999",
                             line = list(color = "#CC0000", width = 2))) %>%
                   add_trace(x = ~df.dataCorrelation$fitted_values, mode = "lines", type = 'scatter',
                             line = list(color = "black")) %>%
                   layout(title = "<b> Correlation Scatter Plot", xaxis = list(title = product1), 
                           yaxis = list(title = product2), showlegend = FALSE)

赠予：

如何在此处找到回归线？？

Answer 1

我认为没有像ggscatter这样的现成函数，很可能您必须手动完成，例如首先拟合线性模型并将值添加到data.frame。

我制作了一个类似于您的数据的data.frame：

set.seed(111)
df.dataCorrelation = data.frame(prod1=runif(50,20,60))
df.dataCorrelation$prod2 = df.dataCorrelation$prod1 + rnorm(50,10,5)

fit = lm(prod2 ~ prod1,data=df.dataCorrelation)
fitdata = data.frame(prod1=20:60)
prediction = predict(fit,fitdata,se.fit=TRUE)
fitdata$fitted = prediction$fit

该行的上下边界仅为1.96 *预测标准误：

fitdata$ymin = fitdata$fitted - 1.96*prediction$se.fit
fitdata$ymax = fitdata$fitted + 1.96*prediction$se.fit

我们计算相关性：

COR = cor.test(df.dataCorrelation$prod1,df.dataCorrelation$prod2)[c("estimate","p.value")]
COR_text = paste(c("R=","p="),signif(as.numeric(COR,3),3),collapse=" ")

并将其放入图中：

library(plotly)

df.dataCorrelation %>%
plot_ly(x = ~prod1) %>%
add_markers(x=~prod1, y = ~prod2) %>%
add_trace(data=fitdata,x= ~prod1, y = ~fitted, 
mode = "lines",type="scatter",line=list(color="#8d93ab")) %>%
add_ribbons(data=fitdata, ymin = ~ ymin, ymax = ~ ymax,
line=list(color="#F1F3F8E6"),fillcolor ="#F1F3F880" ) %>%
layout(
    showlegend = F,
    annotations = list(x = 50, y = 50,
    text = COR_text,showarrow =FALSE)
)

Answer 2

另一个选择是使用ggplotly作为

library(plotly)
ggplotly(
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length))+
  geom_point(color = "#CC0000", shape = 21, size = 2) +
  geom_smooth(method = 'lm') +
  annotate("text", label=paste0("R = ", round(with(iris, cor.test(Sepal.Length, Petal.Length))$estimate, 2),
                                ", p = ", with(iris, cor.test(Sepal.Length, Petal.Length))$p.value), 
x = min(iris$Sepal.Length) + 1, y = max(iris$Petal.Length) + 1, color="steelblue", size=5)+
  theme_classic()
)

R plotly（）：向相关散点图添加回归线

2 个答案: