R语言优雅进行nls模型分析

library(tidyverse)
library(ggpmisc)
library(gginnards)
library(ggtext)

args <- list(formula = y ~ k * e ^ x,
             start = list(k = 1, e = 2))

ggplot(mtcars,aes(wt,mpg)) +
  geom_point() +
  stat_fit_augment(method = "nls",method.args = args) +
  stat_fit_tidy(method = "nls",method.args = args,label.x = "right",
                label.y = "top",
                aes(label = sprintf("\"y\"~`=`~%.3g %%*%% %.3g^{\"x\"}",
                                    after_stat(k_estimate),
                                    after_stat(e_estimate))),parse = TRUE )

nlsFit <- nls(formula=`mpg` ~ k*e^`wt`,
              start = list(k=1,e=2),
              data=mtcars,
              control=nls.control(maxiter=200))

Nonlinear regression model
  model: mpg ~ k * e^wt
   data: mtcars
      k       e 
49.6597  0.7456 
 residual sum-of-squares: 213.5

Number of iterations to convergence: 10 
Achieved convergence tolerance: 2.043e-06

nlsParams <- nlsFit$m$getAllPars()

nlsEqn <- substitute(italic(y) == k %.% e^ italic(x),
                     list(k=format(nlsParams['k'],digits=3),
                          e=format(nlsParams["e"],digits=3)))
dlabel <- tibble(label="y = 49.7*0.746<sup>x</sup>",x=4,y=35)

ggplot(mtcars,aes(wt,mpg)) +
  geom_point()+
  stat_smooth(method = 'nls',
              method.args = list(start = c(a=1, b=1)), 
              formula = y~a*exp(b*x), se = FALSE)+
  geom_richtext(data=dlabel,aes(x=x,y=y,label=label),
                fill=NA,label.color=NA,show.legend = F)+
  theme_bw()