R：当在geom_smooth的自定义函数中使用时，不会选择其他参数 [英] R: nls not picking up additional arguments when used in custom function in geom_smooth

问题描述

这是一个涉及我以前的问题的问题具有facet_grid和不同拟合功能的geom_smooth 。在那个问题中，我试图在ggplot2的facet网格中的每个方面使用 geom_smooth 中的不同拟合函数。 Marco Sandri 善意地提供了一个答案，我试图适应使用用户定义的公式而不是现有的公式（例如， lm ， loess ）。这是我的代码。

This is a question that relates to my earlier question geom_smooth with facet_grid and different fitting functions. In that question, I was trying to use a different fitting function in geom_smooth for each facet in a facet grid for ggplot2. Marco Sandri kindly provided an answer that I am trying to adapt to use user-defined formulas rather than existing formulas (e.g., lm, loess). Here is the my code.

# Load library
library(ggplot2)

# Load data
data(mtcars)

# Smoothing function with different behaviour depending on the panel
custom.smooth <- function(formula, data,...){
  smooth.call <- match.call()

  if(as.numeric(unique(data$PANEL)) == 6) {
    # Nonlinear regression
    method.name <- eval(parse(text="nls"))
    # Specify formula
    formula <- as.formula("y ~ a * x^b")
    # Add initial parameters
    smooth.call[["start"]] <- c(a = 10, b = -0.5)
  }else{
    # Linear regression
    method.name <- eval(parse(text="lm"))
  }

  # Add function name
  smooth.call[[1]] <- method.name
  # Perform fit
  eval.parent(smooth.call)
}

# Plot data with custom fitting function
p <- ggplot(mtcars,aes(x = disp, y = mpg)) + geom_point() + facet_grid(gear ~ am)
p <- p + geom_smooth(method = "custom.smooth")
print(p)

，我定义了一个函数 custom.smooth 来选择适合的模型。在这个例子中，所有的模型都是线性回归，除了面板6，这是一个用户定义的函数 y〜a * x ^ b 。运行此代码会产生以下错误：
$ b

In this code, I define a function custom.smooth that chooses the model to be fit. In this example, all models are linear regressions except for panel 6, which is a user-defined function y ~ a*x^b. Running this code gives the error:

警告消息：计算在 stat_smooth（）中失败：单数
初始参数估计的梯度矩阵

Warning message: Computation failed in stat_smooth(): singular gradient matrix at initial parameter estimates

然而，当我运行 nls code>对这些初始参数的面板6中的数据我没有这样的错误（即， nls（mpg〜a * disp ^ b，mtcars％>％filter（gear == 5 ，am == 1），start = c（a = 10，b = -0.5）））。这让我觉得 nls 没有看到我指定的开始值。我也尝试在 geom_smooth 函数中指定这些参数，如下所示：



Nevertheless, when I run nls on the data in panel 6 with these initial parameters I get no such error (i.e., nls(mpg ~ a * disp^b, mtcars %>% filter(gear == 5, am == 1), start = c(a = 10, b = -0.5))). This makes me think that nls isn't seeing the start values I specify. I have also tried specifying these parameters in the geom_smooth function like this:

p <- p + geom_smooth(method = "custom.smooth", method.args = list(start = c(a = 10, b = -0.5)))

但我碰到同样的问题。任何想法如何让我的初始值为 nls ？还是有另一个原因为什么代码不工作？

but I run into the same issue. Any ideas how I can get my start values to nls? Or is there another reason why the code isn't working?

推荐答案

这是解决方案，它极大地受益于这篇文章。我不知道为什么以前的版本没有工作，但这似乎工作正常。

Here's the solution, which greatly benefited from this post. I don't know why the previous version didn't work, but this seems to work fine.

# Load library
library(ggplot2)

# Load data
data(mtcars)

# Smoothing function with different behaviour depending on the panel
custom.smooth <- function(formula, data,...){
  smooth.call <- match.call()

  if(as.numeric(unique(data$PANEL)) == 6) {
    # Nonlinear regression
    smooth.call[[1]] <- quote(nls)
    # Specify formula
    smooth.call$formula <- as.formula("y ~ a * x ^ b")
    # Add initial parameters
    smooth.call$start <- c(a = 300, b = -0.5)
  }else{
    # Linear regression
    smooth.call[[1]] <- quote(lm)
  }

  # Perform fit
  eval.parent(smooth.call)
}

# Plot data with custom fitting function
p <- ggplot(mtcars,aes(x = disp, y = mpg)) + geom_point() + facet_grid(gear ~ am)
p <- p + geom_smooth(method = "custom.smooth", se = FALSE)
print(p)

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