R：如何从for循环而不是索引输出因子级别？

我有一个数据框，我正在运行蒙特卡罗模拟，使用for循环生成模拟分布。当我测试模拟代码时，我只是访问数据帧中的第一个观察值：

Male.MC <-c()
for (j in 1:100){
    for (i in 1:1)  {
        # u2 <- Male.DistF$Male.stddev_u2[i] * rnorm(1, mean = 0, sd = 1)
        u2 <- Male.DistF$RndmEffct[i] * rnorm(1, mean = 0, sd = 1)
        mc_bca <- Male.DistF$lmefits[i] + u2
        temp <- Lambda.Value*mc_bca+1
        ginv_a <- temp^(1/Lambda.Value)
        d2ginv_a <- max(0,(1-Lambda.Value)*temp^(1/Lambda.Value-2))
        mc_amount <- ginv_a + d2ginv_a * Male.DistF$Male.var[i]^2 / 2
        z <- c(RespondentID <- Male.DistF$RespondentID[i], 
                   Male.DistF$AgeFactor[i], Male.DistF$SampleWeight[i], 
        Male.DistF$Male.var[i], Male.DistF$lmefits[i], u2, mc_amount) 
        Male.MC <- as.data.frame(rbind(Male.MC,z))
    }
}
colnames(Male.MC) <- c("RespondentID", "AgeFactor", 
                       "SampleWeight", "VarByAge", 
                       "lmefits", "u2", "mc_amount")

如何使'Male.MC1数据框包含这两个变量的因子级别？我试过：

z <- c(RespondentID <- as.character(Male.DistF$RespondentID[i]), 
       Male.DistF$AgeFactor[i], Male.DistF$SampleWeight[i], 
       Male.DistF$Male.var[i], Male.DistF$lmefits[i], u2, mc_amount)

对于测试，这里是输入数据帧的前几行

Male.DistF

：

     AgeFactor RespondentID SampleWeight IntakeAmt   RndmEffct NutrientID Gender Age BodyWeight  IntakeDay BoxCoxXY  lmefits      lmeres   TotWts   GrpWts NumSubjects TotSubjects  Male.var
1725     9to13       100020    0.4952835 12145.852  0.30288536        267      1  12       51.6 Day1Intake 15.61196 15.22634  0.27138449 2291.827 763.0604         525        2249 0.4189871
203     14to18       100419    0.3632839  9591.953  0.02703093        267      1  14       46.3 Day1Intake 15.01444 15.31373 -0.18039624 2291.827 472.3106         561        2249 0.3365423

Lambda.Value

为

0.1

。

Male.DistF

的信息如下：

str(Male.DistF)
'data.frame':   2249 obs. of  18 variables:
$ AgeFactor   : Ord.factor w/ 4 levels "1to3"<"4to8"<..: 3 4 3 4 2 2 3 1 1 3 ...
$ RespondentID: Factor w/ 2249 levels "100020","100419",..: 1 2 3 4 5 6 7 8 9 10 ...
$ SampleWeight: num  0.495 0.363 0.495 1.326 2.12 ...
$ IntakeAmt   : num  12146 9592 7839 11113 7150 ...
$ RndmEffct   : num  0.3029 0.027 0.0772 0.4667 -0.1593 ...
$ NutrientID  : int  267 267 267 267 267 267 267 267 267 267 ...
$ Gender      : int  1 1 1 1 1 1 1 1 1 1 ...
$ Age         : int  12 14 11 15 6 5 10 2 2 9 ...
$ BodyWeight  : num  51.6 46.3 46.1 63.2 28.4 18 38.2 14.4 14.6 32.1 ...
$ IntakeDay   : Factor w/ 2 levels "Day1Intake","Day2Intake": 1 1 1 1 1 1 1 1 1 1 ...
$ BoxCoxXY    : num  15.6 15 14.5 15.4 14.3 ...
$ lmefits     : num  15.2 15.3 15 15.8 14.3 ...
$ lmeres      : num  0.271 -0.18 -0.342 -0.424 -0.053 ...
$ TotWts      : num  2292 2292 2292 2292 2292 ...
$ GrpWts      : num  763 472 763 472 779 ...
$ NumSubjects : int  525 561 525 561 613 613 525 550 550 525 ...
$ TotSubjects : int  2249 2249 2249 2249 2249 2249 2249 2249 2249 2249 ...
$ Male.var    : num  0.419 0.337 0.419 0.337 0.267 ...

str（男性DistF）
“data.frame”：2249 obs。在18个变量中：
$AgeFactor:Ord.factor w/4级“1to3”您可以尝试替换该行

z <- c(...

z谢谢你的回答，它工作得很好。我在
RespondentID
和
AgeFactor
变量上使用了
as.character
强制输出我想要的结果。这件事让我头痛了好几个小时。：）
     AgeFactor RespondentID SampleWeight IntakeAmt   RndmEffct NutrientID Gender Age BodyWeight  IntakeDay BoxCoxXY  lmefits      lmeres   TotWts   GrpWts NumSubjects TotSubjects  Male.var
1725     9to13       100020    0.4952835 12145.852  0.30288536        267      1  12       51.6 Day1Intake 15.61196 15.22634  0.27138449 2291.827 763.0604         525        2249 0.4189871
203     14to18       100419    0.3632839  9591.953  0.02703093        267      1  14       46.3 Day1Intake 15.01444 15.31373 -0.18039624 2291.827 472.3106         561        2249 0.3365423

str(Male.DistF)
'data.frame':   2249 obs. of  18 variables:
$ AgeFactor   : Ord.factor w/ 4 levels "1to3"<"4to8"<..: 3 4 3 4 2 2 3 1 1 3 ...
$ RespondentID: Factor w/ 2249 levels "100020","100419",..: 1 2 3 4 5 6 7 8 9 10 ...
$ SampleWeight: num  0.495 0.363 0.495 1.326 2.12 ...
$ IntakeAmt   : num  12146 9592 7839 11113 7150 ...
$ RndmEffct   : num  0.3029 0.027 0.0772 0.4667 -0.1593 ...
$ NutrientID  : int  267 267 267 267 267 267 267 267 267 267 ...
$ Gender      : int  1 1 1 1 1 1 1 1 1 1 ...
$ Age         : int  12 14 11 15 6 5 10 2 2 9 ...
$ BodyWeight  : num  51.6 46.3 46.1 63.2 28.4 18 38.2 14.4 14.6 32.1 ...
$ IntakeDay   : Factor w/ 2 levels "Day1Intake","Day2Intake": 1 1 1 1 1 1 1 1 1 1 ...
$ BoxCoxXY    : num  15.6 15 14.5 15.4 14.3 ...
$ lmefits     : num  15.2 15.3 15 15.8 14.3 ...
$ lmeres      : num  0.271 -0.18 -0.342 -0.424 -0.053 ...
$ TotWts      : num  2292 2292 2292 2292 2292 ...
$ GrpWts      : num  763 472 763 472 779 ...
$ NumSubjects : int  525 561 525 561 613 613 525 550 550 525 ...
$ TotSubjects : int  2249 2249 2249 2249 2249 2249 2249 2249 2249 2249 ...
$ Male.var    : num  0.419 0.337 0.419 0.337 0.267 ...

z <- c(...

z <- data.frame(
  RespondentID = Male.DistF$RespondentID[i], 
  AgeFactor    = Male.DistF$AgeFactor[i], 
  SampleWeight = Male.DistF$SampleWeight[i], 
  VarByAge     = Male.DistF$Male.var[i], 
  lmefits      = Male.DistF$lmefits[i], 
  u2           = u2, 
  mc_amount    = mc_amount
)