ggplot2：将p值添加到图中 [英] ggplot2: add p-values to the plot

问题描述

我得到了这个阴谋

使用

  library（dplyr）
 library（ggplot2）
 library（ggpmisc）
 
 df < -  diamonds％>>％
 dplyr :: filter（cut％in％c（Fair，Ideal））％>％
 dplyr :: filter （清晰度以％c为单位（I1，SI2，SI1，VS2，VS1，VVS2））％>％
 dplyr :: mutate（new_price = ifelse（cut ==公平，
价格* 0.5，
价格* 1.1））
 
公式<  -  y〜x 
 ggplot（df，aes（x = new_price，y = carat，color = cut））+ 
 geom_point（alpha = 0.3）+ 
 facet_wrap（〜clarity，scales =free_y）+ 
 geom_smooth（method =lm ，公式=公式，se = F）+ 
 stat_poly_eq（aes（label = paste（.. rr.label ..）），
 label.x.npc =righ t，label.y.npc = 0.15，
 formula = formula，parse = TRUE，size = 3）
  

除了R2之外，我还想将p值添加到方面。我可以通过首先运行回归然后获取p值并使用 geom_text（）来添加这些p值

I got this plot

Using the code below

library(dplyr) 
library(ggplot2)
library(ggpmisc)

df <- diamonds %>%
  dplyr::filter(cut%in%c("Fair","Ideal")) %>%
  dplyr::filter(clarity%in%c("I1" ,  "SI2" , "SI1" , "VS2" , "VS1",  "VVS2")) %>%
  dplyr::mutate(new_price = ifelse(cut == "Fair", 
                                   price* 0.5, 
                                   price * 1.1))

formula <- y ~ x    
ggplot(df, aes(x= new_price, y= carat, color = cut)) +
  geom_point(alpha = 0.3) +
  facet_wrap(~clarity, scales = "free_y") +
  geom_smooth(method = "lm", formula = formula, se = F) +
  stat_poly_eq(aes(label = paste(..rr.label..)), 
               label.x.npc = "right", label.y.npc = 0.15,
               formula = formula, parse = TRUE, size = 3)

In addition to R2, I want to add p-values to the facets as well. I can do this manually through running the regression first then getting p-values and using geom_text() to add these p-values similar to the answer of this question.

Is there any faster or automated way to do that? e.g. similar to the way R2 values have been added.

Update

The p-value I'm talking about is the slope p-value. The trends are considered highly statistically significant when p < 0.005.

解决方案

Use stat_fit_glance which is part of the ggpmisc package in R. This package is an extension of ggplot2 so it works well with it.

ggplot(df, aes(x= new_price, y= carat, color = cut)) +
       geom_point(alpha = 0.3) +
       facet_wrap(~clarity, scales = "free_y") +
       geom_smooth(method = "lm", formula = formula, se = F) +
       stat_poly_eq(aes(label = paste(..rr.label..)), 
       label.x.npc = "right", label.y.npc = 0.15,
       formula = formula, parse = TRUE, size = 3)+
       stat_fit_glance(method = 'lm',
                       method.args = list(formula = formula),
                       geom = 'text',
                       aes(label = paste("P-value = ", signif(..p.value.., digits = 4), sep = "")),
       label.x.npc = 'right', label.y.npc = 0.35, size = 3)

stat_fit_glance basically takes anything passed through lm() in R and allows it to processed and printed using ggplot2. This website has the rundown of some of the functions like stat_fit_glance: http://rpackages.ianhowson.com/cran/ggpmisc/ . Also I believe this gives model p-value, not slope p-value (in general), which would be different for multiple linear regression. For simple linear regression they should be the same though.

Here is the plot:

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