2

这里只是示例数据：

# generation of correlated data   
matrixCR <- matrix(NA, nrow = 100, ncol = 100)
diag(matrixCR) <- 1
matrixCR[upper.tri (matrixCR, diag = FALSE)] <- 0.5
matrixCR[lower.tri (matrixCR, diag = FALSE)] <- 0.5
matrixCR[1:10,1:10]
L = chol(matrixCR)# Cholesky decomposition
nvars = dim(L)[1]
nobs = 200
set.seed(123)
rM = t(L) %*% matrix(rnorm(nvars*nobs), nrow=nvars, ncol=nobs)
rM1 <- t(rM)
rownames(rM1) <- paste("S", 1:200, sep = "") 
colnames(rM1) <- paste("M", 1:100, sep = "")
# introducing missing value to the dataset 
N <- 2000*0.05 # 5% random missing values 
inds <- round ( runif(N, 1, length(rM1)) )
rM1[inds] <- NA


# using random forest implemented in mice package 
require(mice)
out.imp <- mice(rM1, m = 5, method ="rf")
imp.data <- complete(out.imp)

我收到以下错误：

 iter imp variable
  1   1  M1  M2Error in apply(forest, MARGIN = 1, FUN = function(s) sample(unlist(s),  : 
  dim(X) must have a positive length

我不确定是什么导致了这个问题？

4

1 回答 1

1

正如我在评论中提到的，当method设置为 randomforest ( rf) 时，该mice函数在到达只有一个NA值的列时会抛出错误，但对于任何其他数量的NA值都可以正常运行。

我检查了包作者，这似乎是一个错误。在修复之前，您可以为具有单个NA值的那些列选择不同的插补方法。例如：

# Count number of NA in each column
NAcount = apply(rM1, 2, function(x) sum(is.na(x)))

# Create a vector giving the imputation method to use for each column. 
# Set it to "rf" unless that column has exactly one NA value.
method = rep("rf", ncol(rM1))
method[which(NAcount==1)] = "norm"

# Run the imputation with the new "method" selections
out.imp <- mice(rM1, m = 5, method = method)

我意识到，为了保持一致性，您可能希望对所有列使用相同的插补方法，但如果您设置为使用 randomforest 方法，以上为您提供了一个选项。

于 2014-06-03T15:54:45.353 回答