错误：结果不是以下位置的数据框：

我正在尝试在相当大的数据框上运行拟合函数，该数据框由名为的变量分组"big_group" and 'small_group'。 特别是，我试图获得每个的预测和 coefs 值small_group代替big_group.

也就是说，我试图将这些新列添加到我的新 data.frame 末尾do({功能。

由于缺少数据点或“初始参数估计时的奇异梯度矩阵”错误，某些数据组无法拟合。

所以，我用了tryCatch这篇文章的方法如何忽略错误并继续处理列表项我使用了@Koshke的以下答案

R：捕获“nls”中的错误

OTH，解决这个问题后我遇到了一个error正在说

错误：结果不是位置：3 处的数据框

有一些讨论关于这个错误，但我不知道如何实现我的问题。

这是我的可重现的例子； （这个例子与我的真实数据相似，这就是我构建这样的例子的原因）

library(minpack.lm)
library(dplyr)


set.seed(100)

data.list <- lapply(1:2, function(big_group) {
  xx <- c(sort(runif(5,1,5)),sort(runif(5,-8,-2)), rep(5,2))  ##I intentionall added the last two 5 to get unfitted groups

  yy<- sort(runif(12,0,10))

  small_group <- rep(c('a','b','c'),times=c(5,5,2)) ##small groups in under the big_group

  df <- data.frame(xx,yy,small_group,big_group)

  df <- df%>%
    group_by(big_group,small_group)%>%

  do({
  #fitting part
    fit <- tryCatch(nlsLM(yy~k*xx/2+U, start=c(k=1,U=5), data = ., trace=T, 
                          control = nls.lm.control(maxiter=100)),error=function(e) NULL)

      if(!("NULL" %in% class(fit))){

    new.range<- data.frame(xx=seq(1,10,length.out=nrow(.)))
    predicted <- predict(fit, newdata =new.range)
    coefs <- data.frame(k=coef(fit)[1],U=coef(fit)[2])

    data.frame(., new.range,predicted,coefs,row.names=NULL) ##This is the part the error came from I guess!

}})
})

这就是数据的样子； @RomanLuštrik

data.list <- lapply(1:2, function(big_group) {
  xx <- c(sort(runif(5,1,5)),sort(runif(5,-8,-2)), rep(5,2))  ##I intentionall added the last two 5 to get unfitted groups
  yy<- sort(runif(12,0,10))
  small_group <- rep(c('a','b','c'),times=c(5,5,2)) ##small groups in under the big_group
  df <- data.frame(xx,yy,small_group,big_group)
})


df <- bind_rows(data.list)
 > df
          xx       yy small_group big_group
1   1.685681 1.302889           a         1
2   2.680406 1.804072           a         1
3   3.153395 3.306605           a         1
4   3.995889 3.486920           a         1
5   4.081206 6.293909           a         1
6  -6.333657 6.952741           b         1
7  -5.070164 7.775844           b         1
8  -4.705420 8.273034           b         1
9  -2.708278 8.651205           b         1
10 -2.428970 8.894535           b         1
11  5.000000 9.541577           c         1
12  5.000000 9.895641           c         1
13  1.830856 1.234872           a         2
14  2.964927 2.114086           a         2
15  3.413297 2.299059           a         2
16  4.121434 2.533907           a         2
17  4.536908 3.577738           a         2
18 -6.807926 4.451480           b         2
19 -6.585834 4.637012           b         2
20 -6.350680 5.913211           b         2
21 -6.157485 5.975753           b         2
22 -6.016821 6.471012           b         2
23  5.000000 6.763982           c         2
24  5.000000 9.605731           c         2

这个怎么样？问题似乎在于迫使传统的 R 代码与%>%管道，所以我只是解决它。

# Libraries and Options ---------------------------------------------------
library(minpack.lm)
library(dplyr)
set.seed(100)

# Create the data ---------------------------------------------------------
data.list <- lapply(1:2, function(big_group) {
  xx <- c(sort(runif(5,1,5)),sort(runif(5,-8,-2)), rep(5,2))  ##I intentionall added the last two 5 to get unfitted groups

  yy<- sort(runif(12,0,10))

  small_group <- rep(c('a','b','c'),times=c(5,5,2)) ##small groups in under the big_group

  df <- data.frame(xx,yy,small_group,big_group)
})

df <- bind_rows(data.list)



# Fit the Model -----------------------------------------------------------
print("My understanding here is that you want a separate model fit for each combination of big group and small group")

# Create fit-level groups
df$big_small <- paste0(df$big_group, df$small_group)

# Create results object
df1 <- structure(list(xx = numeric(0), yy = numeric(0), small_group = structure(integer(0), .Label = c("a", 
                      "b", "c"), class = "factor"), big_group = integer(0), big_small = character(0), 
                      xx.1 = numeric(0), predicted = numeric(0), k = numeric(0), 
                      U = numeric(0)), .Names = c("xx", "yy", "small_group", "big_group", 
                                                  "big_small", "xx.1", "predicted", "k", "U"), row.names = integer(0), class = "data.frame")

# Fit model, get results
for(b_s in unique(df$big_small)){
  fit <- tryCatch(nlsLM(yy~k*xx/2+U, start=c(k=1,U=5), data = df[df$big_small==b_s,], trace=T, 
                        control = nls.lm.control(maxiter=100)),error=function(e) NULL)

  if(!("NULL" %in% class(fit))){

    new.range<- data.frame(xx=seq(1,10,length.out=nrow(df[df$big_small==b_s,])))
    predicted <- predict(fit, newdata =new.range)
    coefs <- data.frame(k=coef(fit)[1],U=coef(fit)[2])

    df1 <- rbind(df1, data.frame(df[df$big_small==b_s,], new.range,predicted,coefs,row.names=NULL))
  }   
}

It.    0, RSS =    44.4318, Par. =          1          5
It.    1, RSS =   0.259895, Par. =    1.89421    1.00916
It.    2, RSS =   0.259895, Par. =    1.89421    1.00916
It.    0, RSS =    81.5517, Par. =          1          5
It.    1, RSS =   0.256959, Par. =   0.912615    8.80728
It.    2, RSS =   0.256959, Par. =   0.912615    8.80728
It.    0, RSS =    1.76253, Par. =          1          5
It.    1, RSS =   0.715381, Par. =   -156.969    400.646
It.    2, RSS =   0.715381, Par. =   -156.969    400.646
It.    0, RSS =     64.766, Par. =          1          5
It.    1, RSS =    4.27941, Par. =    3.32947   -1.95395
It.    2, RSS =    4.27941, Par. =    3.32947   -1.95395
It.    0, RSS =     137.22, Par. =          1          5
It.    1, RSS =   0.209219, Par. =   0.893139    10.0071
It.    2, RSS =   0.209219, Par. =   0.893139    10.0071
It.    0, RSS =    9.90713, Par. =          1          5
It.    1, RSS =  0.0626808, Par. =    -156.67    401.394
It.    2, RSS =  0.0626808, Par. =    -156.67    401.394

df1

          xx       yy small_group big_group big_small  xx.1  predicted         k         U
1   1.225533 2.046122           a         1        1a  1.00  1.9562669 1.8942075  1.009163
2   2.030690 2.803538           a         1        1a  3.25  4.0872502 1.8942075  1.009163
3   2.231064 3.575249           a         1        1a  5.50  6.2182336 1.8942075  1.009163
4   2.874197 3.594751           a         1        1a  7.75  8.3492170 1.8942075  1.009163
5   3.209290 3.984879           a         1        1a 10.00 10.4802004 1.8942075  1.009163
6  -6.978428 5.358112           b         1        1b  1.00  9.2635844 0.9126145  8.807277
7  -5.778077 6.249965           b         1        1b  3.25 10.2902757 0.9126145  8.807277
8  -5.097376 6.690217           b         1        1b  5.50 11.3169671 0.9126145  8.807277
9  -4.720648 6.902905           b         1        1b  7.75 12.3436585 0.9126145  8.807277
10 -3.125584 7.108038           b         1        1b 10.00 13.3703498 0.9126145  8.807277
11  1.685681 1.302889           a         2        2a  1.00 -0.2892182 3.3294688 -1.953953
12  2.680406 1.804072           a         2        2a  3.25  3.4564342 3.3294688 -1.953953
13  3.153395 3.306605           a         2        2a  5.50  7.2020866 3.3294688 -1.953953
14  3.995889 3.486920           a         2        2a  7.75 10.9477390 3.3294688 -1.953953
15  4.081206 6.293909           a         2        2a 10.00 14.6933913 3.3294688 -1.953953
16 -6.333657 6.952741           b         2        2b  1.00 10.4536476 0.8931386 10.007078
17 -5.070164 7.775844           b         2        2b  3.25 11.4584286 0.8931386 10.007078
18 -4.705420 8.273034           b         2        2b  5.50 12.4632095 0.8931386 10.007078
19 -2.708278 8.651205           b         2        2b  7.75 13.4679905 0.8931386 10.007078
20 -2.428970 8.894535           b         2        2b 10.00 14.4727715 0.8931386 10.007078