您是否考虑过将代码更改为函数？您可以将所有data. frame存储在一个命名列表中，然后对列表中的每个data.frame应用函数，然后收集结果。

library(tidyverse)
library(broom)

# changing your code to a function
my_function <- function(.df){
  
  nls.mon <- nls(wt~A*(1-exp(k*(t0-age))), 
                 data=.df,
                 start = list(A=253.6,k=.03348,t0=32.02158))
  
  nls.von <- nls(wt ~A*(1-(1/3)*exp(k*(t0-age)))^3, 
                 data=.df,
                 start= list(A=253.6,k=.03348,t0=32.02158))
  
  # I slightly edited this part of your code
  df <- bind_rows(
    tidy(nls.mon) %>% mutate(model = "nls.mon"),
    tidy(nls.von) %>% mutate(model = "nls.von")
  ) %>%
    select(model, term, estimate) %>%
    pivot_wider(names_from = "term", values_from = "estimate")

  return(df)
}

# reading in data, the path to the data needs to be changed
df1 <- read_csv(r"{C:\Users\novot\Downloads\sample.csv}") %>%
  select(-1)

df2 <- df1 %>%
  filter(sex == "M")

# using map to apply the created function to each member of the list
df_out <- list("df1" = df1, "df2" = df2) %>%
  map(
    ~my_function(.x)
  ) %>%
  bind_rows(.id = "dataframe")

df_out
#> # A tibble: 4 x 5
#>   dataframe model       A      k    t0
#>   <chr>     <chr>   <dbl>  <dbl> <dbl>
#> 1 df1       nls.mon  248. 0.0135  2.09
#> 2 df1       nls.von  246. 0.0222 32.9 
#> 3 df2       nls.mon  248. 0.0135  2.09
#> 4 df2       nls.von  246. 0.0222 32.9

您可以将代码 Package 在函数中，然后将该函数应用于 Dataframe 列表

f <- function(n,dfs) {
  nls.aad_arc <- nls(wt~ B*atan(k*(age - mx)) + my, data=dfs[[n]],start = list(B=120, k=.02, mx=30, my= 82.06)) 
  aad_arc_est <- data.frame(tidy(nls.aad_arc)) 
  data.frame(
    dataframe = n,
    aad_arc_B_est  <- as.numeric(aad_arc_est[1, "estimate"]), 
    aad_arc_k_est  <- as.numeric(aad_arc_est[2, "estimate"]),
    aad_arc_mx_est <- as.numeric(aad_arc_est[3, "estimate"])  
  )
}

dfs = list('df1'=df1, 'df2'=df2,'df3' = df3)
do.call(rbind, lapply(names(dfs), function(x) f(x,dfs)))

最适合我们的解决方案是这个（它有点长，但也很灵活/用户友好）。
我们不仅对初始参数（A，t0，k）进行了计算，而且还可以应用于反正切函数之外的其他方程。

#Dataframe and variable names 
dfs      <- list(aad, fad, mad, fnm, fma, mnm, mma) #List of dataframes for analysis 
df_names <- list('aad', 'fad', 'mad', 'fnm', 'fma', 'mnm', 'mma') #list of dataframe names
p_names  <- list('df', 'mod', 'A_val', 'A_val_se', 'B_val', 'B_val_se', 'k_val', 'k_val_se', 't0_val', 't0_val_se', 'm_val', 'm_val_se', 'mx_val', 'mx_val_se', 'my_val', 'my_val_se', 'ran_A_max', 'ran_A_min', 'ran_B_max', 'ran_B_min', 'ran_k_max', 'ran_k_min', 'ran_t0_max', 'ran_t0_min', 'ran_m_max', 'ran_m_min', 'ran_mx_max', 'ran_mx_min', 'ran_my_max', 'ran_my_min') #List of parameters and values
m_names  <- list('arc') #List of model names 
df_num   <- length(dfs) #Number of dataframes
var_num  <- length(p_names) #Number of variables in our table  
mod_num  <- length(m_names) #Number of growth models

#nlme direct output variable names 
arc_names <- list("arc_aad", "arc_fad", "arc_mad", "arc_fnm", "arc_fma", "arc_mnm", "arc_mma")   #list of variable names for nlme.arc outputs

#Creates blank dataframe 
s_pmod <- data.frame(matrix(ncol = var_num, nrow = df_num*mod_num)) #Sets dataframe size from number of models, parameters, and datasets  
colnames(s_pmod) <- p_names #Sets column names 
s_pmod #View blank dataframe 

#Creates dataframe for nlme.arc outputs
nlme_arc.out  <- data.frame(matrix(ncol = df_num, nrow = 1)) #Sets dataframe size from number of models, parameters, and datasets  
colnames(s_pmod) <- p_names #Sets column names 

#Runs each model over all dataframes and appends values into s_pmod    
for (i in 1:df_num) {    
#print(dfs[[i]]) #View full dataframes  

#Arctangent     
#nls fit for determining best initial guess for nlme model 
nls.arc <- nls(wt~ B*atan(k*(age - mx)) + my, #Function
    data = dfs[[i]], #Input dataframe 
    start = list(B = 120, k = .02, mx = 30, my= 82.2)) #Initial guesses  

arc_est <- data.frame(tidy(nls.arc)) #Converts nls output into dataframe
arc_A_est  <- as.numeric(arc_est[1, "estimate"]) #Extracts parameters from dataframe for initial guesses
arc_k_est  <- as.numeric(arc_est[2, "estimate"])
arc_mx_est <- as.numeric(arc_est[3, "estimate"])
arc_my_est <- as.numeric(arc_est[4, "estimate"]) 

#non linear least squares fit
nlme.arc <- nlme(wt ~ (B*atan(k*(age - mx)) + my), #Function
    data=dfs[[i]], #Input dataset 
    fixed=B+k+mx+my~1, #Fixed effects
    random=list(squirrel_id = pdDiag(B+k+mx+my~1)), #pdDiag specifies random affects are uncorrilated  
    start=c(B=arc_A_est, k=arc_k_est, mx=arc_mx_est, my=arc_my_est), #Initial guesses from nls calcuations
    na.action=na.omit, #Omit any NA values
    control=nlmeControl(maxIter=200, pnlsMaxIter=10, msMaxIter=100)) #Maximum number of itterations before determined divergent

#Assign nlme.arc outputs to a unique variable name
assign(arc_names[[i]], nlme.arc)   

#Extracts and define parameters and from nlme output and appends into s_pmod
arc_out <- data.frame(tidy(nlme.arc)) #Creates df with nlme model outputs
s_pmod[(1+mod_num*(i-1)), 'B_val']  <- as.numeric(arc_out[1, "estimate"]) #Extracts individual model outputs as numerics 
s_pmod[(1+mod_num*(i-1)), 'k_val']  <- as.numeric(arc_out[2, "estimate"])  
s_pmod[(1+mod_num*(i-1)), 'mx_val'] <- as.numeric(arc_out[3, "estimate"])
s_pmod[(1+mod_num*(i-1)), 'my_val'] <- as.numeric(arc_out[4, "estimate"])
s_pmod[(1+mod_num*(i-1)), 'A_val']  <- ((as.numeric(arc_out[1, "estimate"])*pi)/2)+ as.numeric(arc_out[4, "estimate"]) #Calculates and appends upper asymptote for arctangent

#Appends parameter standard error measurements into s_pmod
arc_out <- data.frame(tidy(nlme.arc)) #Creates df with nlme model outputs
s_pmod[(1+mod_num*(i-1)), 'B_val_se']  <- as.numeric(arc_out[1, "std.error"]) #Extracts parameter standard error and appends into s_pmod  
s_pmod[(1+mod_num*(i-1)), 'k_val_se']  <- as.numeric(arc_out[2, "std.error"])  
s_pmod[(1+mod_num*(i-1)), 'mx_val_se'] <- as.numeric(arc_out[3, "std.error"])
s_pmod[(1+mod_num*(i-1)), 'my_val_se'] <- as.numeric(arc_out[4, "std.error"])
s_pmod[(1+mod_num*(i-1)), 'A_val_se']  <- (as.numeric(arc_out[1, "std.error"])*pi)/2 #Calculates and appends standard error of parameter A

#Extracts random effects and appends values into s_pmod 
arc_ran <- ranef(nlme.arc) #Extracts all random effects in the dataframe 
s_pmod[(1+mod_num*(i-1)), 'ran_B_max']  <- max(arc_ran$B) #Extracts and appends maximum random effect for parameter
s_pmod[(1+mod_num*(i-1)), 'ran_B_min']  <- min(arc_ran$B) #Extracts and appends minimum random effect for parameter 
s_pmod[(1+mod_num*(i-1)), 'ran_k_max']  <- max(arc_ran$k)
s_pmod[(1+mod_num*(i-1)), 'ran_k_min']  <- min(arc_ran$k)
s_pmod[(1+mod_num*(i-1)), 'ran_mx_max'] <- max(arc_ran$mx)
s_pmod[(1+mod_num*(i-1)), 'ran_mx_min'] <- min(arc_ran$mx)
s_pmod[(1+mod_num*(i-1)), 'ran_my_max'] <- max(arc_ran$my)
s_pmod[(1+mod_num*(i-1)), 'ran_my_min'] <- min(arc_ran$my)

s_pmod[(1+mod_num*(i-1)), 'df']         <- df_names[i] #Appends dataframe name

}#End of loop

输出如下：

s_pmod %>% filter(mod=="arc")
   df mod    A_val  A_val_se    B_val  B_val_se      k_val     k_val_se t0_val t0_val_se m_val m_val_se   mx_val mx_val_se   my_val my_val_se ran_A_max ran_A_min ran_B_max ran_B_min   ran_k_max
1 aad arc 253.9916 0.7015467 107.1339 0.4466185 0.02322817 0.0001367976     NA        NA    NA       NA 38.51732 0.2957660 85.70604 0.5693539        NA        NA  33.14024 -31.18713 0.005225140
2 fad arc 260.7412 1.1965906 144.9240 0.7617732 0.01653624 0.0001315679     NA        NA    NA       NA 13.69431 0.4264706 33.09517 1.0042012        NA        NA  39.99318 -30.58696 0.011562276
3 mad arc 259.7367 0.8902014 105.3544 0.5667198 0.02458354 0.0002119215     NA        NA    NA       NA 42.28176 0.3671864 94.24638 0.7103262        NA        NA  19.11504 -31.07500 0.005268441
4 fnm arc 250.3786 1.1041547 105.6588 0.7029267 0.02316940 0.0002086655     NA        NA    NA       NA 38.23024 0.4760433 84.41015 0.9010855        NA        NA  22.95082 -20.57937 0.003980173
5 fma arc 260.0539 2.0168075 148.5924 1.2839396 0.01517521 0.0002634139     NA        NA    NA       NA 10.76892 0.7676714 26.64548 1.7216544        NA        NA  40.69058 -28.85547 0.011894187
6 mnm arc 260.5745 1.0445654 104.6731 0.6649910 0.02487617 0.0002508612     NA        NA    NA       NA 43.30369 0.4131035 96.15442 0.7882765        NA        NA  18.67611 -33.10133 0.005059236
7 mma arc 258.7071 1.6936251 107.2850 1.0781952 0.02405637 0.0003971414     NA        NA    NA       NA 40.17088 0.7339977 90.18416 1.4159323        NA        NA  19.59260 -18.45392 0.005446648
     ran_k_min ran_t0_max ran_t0_min ran_m_max ran_m_min      ran_mx_max       ran_mx_min      ran_my_max      ran_my_min
1 -0.007378940         NA         NA        NA        NA 0.0000105015210 -0.0000150388369 21.817420360305 -19.49415109139
2 -0.012402311         NA         NA        NA        NA 0.0000024081791 -0.0000027762636  0.000001706597  -0.00000156346
3 -0.009515597         NA         NA        NA        NA 1.6232147679678 -2.1681922266485 24.362426128753 -24.18797605980
4 -0.004196356         NA         NA        NA        NA 0.0000244818069 -0.0000307554261 15.925214720426 -13.92832902269
5 -0.011169350         NA         NA        NA        NA 0.0000007861126 -0.0000006581591  0.000124799561  -0.00012778252
6 -0.009676215         NA         NA        NA        NA 5.8551715194943 -6.5436633258442 18.513325143935 -22.03287338292
7 -0.005949639         NA         NA        NA        NA 0.0000019910474 -0.0000021254555 20.710967774449 -13.82431195267