为geom_平滑gam中的每条线获取调整后的r平方值

我使用ggplot2生成了下图

PlotEchi = ggplot(data=Echinoidea, 
                  aes(x=Year, y=mean, group = aspect, linetype = aspect, shape=aspect)) + 
  geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.025, position=pd) + 
  geom_point(position=pd, size=2) + 
  geom_smooth(method = "gam", formula = y~s(x, k=3), se=F, size = 0.5,colour="black") + 
  xlab("") + 
  ylab("Abundance (mean +/- SE)") + 
  facet_wrap(~ species, scales = "free", ncol=1) + 
  scale_y_continuous(limits=c(min(y=0), max(Echinoidea$mean+Echinoidea$se))) + 
  scale_x_continuous(limits=c(min(Echinoidea$Year-0.125), max(Echinoidea$Year+0.125)))

---

我想做的是轻松检索每个拟合线的调整后的R平方，而无需使用

model对每个绘制线执行单独的
mgcv:：gam
，这实际上是不可能的，因为ggplot2会丢弃拟合对象。你可以看到这个

1.通过修补ggplot2解决问题
一个难看的解决方法是动态修补ggplot2代码以打印结果。你可以这样做。最初的赋值会抛出一个错误，但无论如何都是有效的。要撤消此操作，只需重新启动R会话

library(ggplot2)

# assignInNamespace patches `predictdf.glm` from ggplot2 and adds 
# a line that prints the summary of the model. For some reason, this
# creates an error, but things work nonetheless.
assignInNamespace("predictdf.glm", function(model, xseq, se, level) {
  pred <- stats::predict(model, newdata = data.frame(x = xseq), se.fit = se,
                         type = "link")

  print(summary(model)) # this is the line I added

  if (se) {
    std <- stats::qnorm(level / 2 + 0.5)
    data.frame(
      x = xseq,
      y = model$family$linkinv(as.vector(pred$fit)),
      ymin = model$family$linkinv(as.vector(pred$fit - std * pred$se.fit)),
      ymax = model$family$linkinv(as.vector(pred$fit + std * pred$se.fit)),
      se = as.vector(pred$se.fit)
    )
  } else {
    data.frame(x = xseq, y = model$family$linkinv(as.vector(pred)))
  }
}, "ggplot2")


控制台输出：

Family: gaussian 
Link function: identity 

Formula:
y ~ s(x, bs = "cs")

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.4280     0.0365   93.91   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
       edf Ref.df     F  p-value    
s(x) 1.546      9 5.947 5.64e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.536   Deviance explained = 55.1%
GCV = 0.070196  Scale est. = 0.066622  n = 50

Family: gaussian 
Link function: identity 

Formula:
y ~ s(x, bs = "cs")

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.77000    0.03797   72.96   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
       edf Ref.df     F  p-value    
s(x) 1.564      9 1.961 8.42e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.268   Deviance explained = 29.1%
GCV = 0.075969  Scale est. = 0.072074  n = 50

Family: gaussian 
Link function: identity 

Formula:
y ~ s(x, bs = "cs")

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.97400    0.04102    72.5   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
       edf Ref.df     F p-value   
s(x) 1.279      9 1.229   0.001 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.191   Deviance explained = 21.2%
GCV = 0.088147  Scale est. = 0.08413   n = 50

正如您所看到的，在这两种情况下提取的R平方值完全相同。
Hi Claus，感谢您的回答。我尝试使用您的tidyverse方法从我的数据中生成结果，我在mutate_impl（.data，dots）中得到错误：求值错误：数值“envir”arg的长度不是1。不知道为什么。我可以复制你的例子。如果你能提供进一步的帮助，我将不胜感激。乔治，这听起来像是stackoverflow的另一个问题。在任何情况下，如果没有一个可复制的例子，没有完整的代码和数据，就不可能提供具体的建议。嗨，克劳斯，我解决了这个问题……在我这方面是一个愚蠢的错误。但是，我现在遇到了一个问题，即运行脚本rsquare值的输出没有显示。。这是一个输出。我把它作为另一个问题贴在这里。对于
R.square=…
语句，使用
map\u dbl（）
而不是
map（）
。我将在这里编辑我的答案并更正它。
Family: gaussian 
Link function: identity 

Formula:
y ~ s(x, bs = "cs")

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.4280     0.0365   93.91   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
       edf Ref.df     F  p-value    
s(x) 1.546      9 5.947 5.64e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.536   Deviance explained = 55.1%
GCV = 0.070196  Scale est. = 0.066622  n = 50

Family: gaussian 
Link function: identity 

Formula:
y ~ s(x, bs = "cs")

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.77000    0.03797   72.96   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
       edf Ref.df     F  p-value    
s(x) 1.564      9 1.961 8.42e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.268   Deviance explained = 29.1%
GCV = 0.075969  Scale est. = 0.072074  n = 50

Family: gaussian 
Link function: identity 

Formula:
y ~ s(x, bs = "cs")

Parametric coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.97400    0.04102    72.5   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Approximate significance of smooth terms:
       edf Ref.df     F p-value   
s(x) 1.279      9 1.229   0.001 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

R-sq.(adj) =  0.191   Deviance explained = 21.2%
GCV = 0.088147  Scale est. = 0.08413   n = 50

library(tidyverse)
library(broom)
iris %>% nest(-Species) %>% 
  mutate(fit = map(data, ~mgcv::gam(Sepal.Width ~ s(Sepal.Length, bs = "cs"), data = .)),
         results = map(fit, glance),
         R.square = map_dbl(fit, ~ summary(.)$r.sq)) %>%
  unnest(results) %>%
  select(-data, -fit)

#      Species  R.square       df    logLik      AIC      BIC deviance df.residual
# 1     setosa 0.5363514 2.546009 -1.922197 10.93641 17.71646 3.161460    47.45399
# 2 versicolor 0.2680611 2.563623 -3.879391 14.88603 21.69976 3.418909    47.43638
# 3  virginica 0.1910916 2.278569 -7.895997 22.34913 28.61783 4.014793    47.72143