拟合 ggplot2、geom_smooth 和 nls [英] Fitting with ggplot2, geom_smooth and nls

问题描述

我试图用方程拟合指数衰减函数(RC 类系统)上的数据:

I am trying to fit data on an exponential decay function (RC like system) with equation:

我的数据位于以下数据框中:

My data are on the following dataframe:

dataset <- data.frame(Exp = c(4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6), t = c(0, 0.33, 0.67, 1, 1.33, 1.67, 2, 4, 6, 8, 10, 0, 33, 0.67, 1, 1.33, 1.67, 2, 4, 6, 8, 10, 0, 0.33, 0.67, 1, 1.33, 1.67, 2, 4, 6, 8, 10), fold = c(1, 0.957066345654286, 1.24139015724819, 1.62889151698633, 1.72008539595879, 1.82725412314402, 1.93164365299958, 1.9722929538061, 2.15842019312484, 1.9200507796933, 1.95804730344453, 1, 0.836176542548747, 1.07077717914707, 1.45471712491441, 1.61069357875771, 1.75576377806756, 1.89280913889538, 2.00219054189937, 1.87795513639311, 1.85242493827193, 1.7409346372629, 1, 0.840498729335292, 0.904130905000499, 1.23116185602517, 1.41897551928886, 1.60167656534099, 1.72389226836308, 1.80635095956481, 1.76640786872057, 1.74327897001172, 1.63581509884482))

我有 3 个实验(Exp:4、5 和 6)数据，我想将每个实验拟合到给定的方程上.

I have 3 experiment (Exp: 4, 5 and 6) data I want to fit each experiment on the given equation.

通过对数据进行子集化并使用 nls 计算的参数，我设法为实验做到了这一点

I have managed to do it for of the experiment by subsetting my data and using the parameter calculated by nls

test <- subset(dataset,Exp==4)
fit1 = nls(fold ~ 1+(Vmax*(1-exp(-t/tau))),
  data=test,
  start=c(tau=0.2,Vmax=2))
ggplot(test,aes(t,fold))+
  stat_function(fun=function(t){1+coef(fit1)[[2]]*(1-exp(-t/coef(fit1)[[1]]))})+
  geom_point()

但是如果我尝试使用此代码直接在完整数据集上使用 geom_smooth 函数

But if I try to use the geom_smooth function directly on the full dataset with this code

d <- ggplot(test,aes(t,fold))+
   geom_point()+
   geom_smooth(method="nls", 
     formula='fold~1+Vmax*(1-exp(-t/tau))',
     start=c(tau=0.2,Fmax=2))
print(d)

我收到以下错误:

Error in model.frame.default(formula = ~fold, data = data, weights = weight) : 
  variable lengths differ (found for '(weights)')
In addition: Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf

我的语法有问题吗?我会让这个工作，以便在 dataset 上使用相同的功能，并使用 group 使每个 Exp 级别都适合.

Is there anything wrong with my syntax? I would have this one working in order to use the same function on the dataset and using group to have one fit per Exp level.

推荐答案

有几个问题:

1. formula 是 nls 的一个参数，你需要给它传递一个公式对象而不是一个字符.
2. ggplot2 将 y 和 x 传递给 nls 而不是 fold 和 t.
3. 默认情况下，stat_smooth 会尝试获取置信区间.这在 predict.nls 中没有实现.

1. formula is a parameter of nls and you need to pass a formula object to it and not a character.
2. ggplot2 passes y and x to nls and not fold and t.
3. By default, stat_smooth tries to get the confidence interval. That isn't implemented in predict.nls.

总结:

d <- ggplot(test,aes(x=t, y=fold))+ 
         #to make it obvious I use argument names instead of positional matching
  geom_point()+
  geom_smooth(method="nls", 
              formula=y~1+Vmax*(1-exp(-x/tau)), # this is an nls argument, 
                                                #but stat_smooth passes the parameter along
              start=c(tau=0.2,Vmax=2), # this too
              se=FALSE) # this is an argument to stat_smooth and 
                        # switches off drawing confidence intervals

在 ggplot2 主要更新到版本 2 后，您需要:

After the major ggplot2 update to version 2, you need:

geom_smooth(method="nls", 
              formula=y~1+Vmax*(1-exp(-x/tau)), # this is an nls argument
              method.args = list(start=c(tau=0.2,Vmax=2)), # this too
              se=FALSE)

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