ggplot2中的nls和对数刻度 [英] nls and log scale in ggplot2

问题描述

我试图在ggplot2中绘制3个非线性模型. 它在自动缩放下工作，但在log10缩放下却无法工作，在此我得到奇异梯度误差".可能是什么问题?

I'm trying to plot 3 non-linear models in ggplot2. It's working in automatic scale but not in log10 scale where I get the "singular gradient error". What could be the problem(s)?

我要拟合的数据(df1):

The data I'm trying to fit (df1):

x   y
4.17    0.55
10.08   0.48
40.25   0.38
101.17  0.32
400.33  0.24

我尝试的代码:

plot <- ggplot(df1, aes(x=x, y=y))+
  stat_smooth(method="nls",
              formula=y~I(a*x^(-n)),
              data=df1,
              start=list(a=1,n=1),
              se=FALSE,
              colour="red")+
  stat_smooth(method="nls",
              formula=y~m*(x+m^(1/n))^(-n),
              data=df1,
              start=list(m=0.7, n=0.17),
              se=FALSE,
              colour="blue")+
  stat_smooth(method="nls",
              formula=y~m*(x+m^(1/n))^(-n)+b,
              data=df1,
              start=list(m=0.7, n=0.17, b=1),
              se=FALSE,
              colour="green")+
  geom_point()+
  scale_x_log10()+
  scale_y_log10()+
  theme_bw()
plot

推荐答案

问题似乎是，当您指定scale_x_log10或scale_y_log10时，数据值在传递给不同的统计信息或宝石.这意味着，尽管您的nls可以处理未转换的数据，但不适用于日志转换的数据.

The problem seems to be that when you specify scale_x_log10 or scale_y_log10, the values of your data are transformed before being passed along to the different stats or geoms. This means while your nls may work on the untransformed data, it does not work on the log-transformed data.

#OK
nls(y~m*(x+m^(1/n))^(-n), df1, start=list(m=0.7, n=0.17))
#NOT OK
nls(y~m*(x+m^(1/n))^(-n), log10(df1), start=list(m=0.7, n=0.17))

在ggplot2中似乎没有什么可以解决的.相反，您可以提前在未转换的比例尺上拟合NLS模型，并使用ggplot2绘制结果.例如

There doesn't seem to be much you can do in ggplot2 to fix this. Instead, you could fit the NLS models ahead of time on the untransformed scale and just plot the results with ggplot2. For example

mods<-list(
    list(y~I(a*x^(-n)), list(a=1,n=1)),
    list(y~m*(x+m^(1/n))^(-n), list(m=0.7, n=0.17)),
    list(y~m*(x+m^(1/n))^(-n)+b, list(m=0.7, n=0.17, b=1))
)

fits<-lapply(mods, function(x, xr) {
    mod<-nls(x[[1]], data=df1, start=x[[2]])
    xx<-seq(xr[1], xr[2], length.out=100)
    yy<-predict(mod, newdata=data.frame(x=xx))
    data.frame(x=xx, y=yy)
}, xr=range(df1$x))

library(ggplot2)
ggplot(df1, aes(x=x, y=y))+
  geom_line(data=fits[[1]], color="red") +
  geom_line(data=fits[[2]], color="blue") +
  geom_line(data=fits[[3]], color="green") +
  geom_point()+
  scale_x_log10()+
  scale_y_log10()+
  theme_bw()

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