Question

我想要归咎于一些数据。我使用包 mvoutlier 中的数据 moss 。目标是将值＆lt;来自Bi列0.004。因为 moss 日期是组合数据，所以我使用包 robCompositions 中的方法。当我试图估算值时，我得到一个错误。

代码：

    library(mvoutlier)
    library(robCompositions)
    data(moss)
    attach(moss)

    x <- moss[-c(1,2,3)] # copying the data from moss, withoud the first 3 variables into x
    x$Bi[Bi < 0.004] <- 0 # the values that are under 0.004 are replaced with 0
    res <- impRZilr(x,dl=c(0,0,0,0,0,0.004,rep(0,25)))
    |=======                                                               |  10%Error in !all.equal(x[!w], xOrig[!w]) : invalid argument type

不知道如何处理此错误

Answer 1

library(mvoutlier)
library(robCompositions)
data(moss)

x <- moss[-c(1,2,3)] #copying the data from moss, withoud the first 3 variables into x
### Before
head(x$Bi)
## [1] 0.002 0.039 0.012 0.033 0.002 0.052

# Impute below 0.004
x$Bi[x$Bi < 0.004] <- 0

## head(x$Bi)
## [1] 0.000 0.039 0.012 0.033 0.000 0.052

# Imputation
result <- impRZilr(x, dl = rep(0.004, nrow(x)))
res <- data.frame(result$x)

head(res$Bi)
## [1] 0.002515667 0.039000000 0.012000000 0.033000000 0.002836172 0.052000000

如您所见，0的值被impRZilr函数值替换。

修改

以下是如何根据评论中的要求访问结果的说明。

# Imputation # Use the verbose = TRUE option to see how the algorithm is iterating result <- impRZilr(x, dl = rep(0.004, nrow(x)), verbose = TRUE) ### Results description str(result) # List of 7 # $ x : num [1:598, 1:31] 0.016 0.073 0.032 0.118 0.038 ... # ..- attr(*, "dimnames")=List of 2 # .. ..$ : NULL # .. ..$ : chr [1:31] "Ag" "Al" "As" "B" ... # $ criteria: num 0.0203 # $ iter : num 4 # $ maxit : num 10 # $ wind : logi [1:598, 1:31] FALSE FALSE FALSE FALSE FALSE FALSE ... # ..- attr(*, "dimnames")=List of 2 # .. ..$ : chr [1:598] "1" "2" "3" "4" ... # .. ..$ : chr [1:31] "U" "Bi" "Th" "Tl" ... # $ nComp : int [1:4] 4 6 3 5 # $ method : chr "pls" # - attr(*, "class")= chr "replaced" # Results data.frame with imputed ceros res <- data.frame(result$x) # Index of missing values index_missing_wind <- data.frame(result$wind) # Number of iterations result$iter # [1] 4 # Method used (you can change this) result$method

Answer 2

OP在编辑中写道：

我设法解决了这个问题，这就是我所做的：
   x <-moss[-c(1,2,3)]
   x$Bi[Bi <- 0.004] <- NA
   res <- impAll(x)
并且对象 res 包含插补矩阵。

估算缺失值

2 个答案: