这只是示例数据:
# 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
我不确定是什么引起了这个问题?
正如我在评论中提到的那样,当将method
设置为randomforest(rf
)时,该mice
函数每当到达只有一个NA
值的列时都会抛出错误,但在任何其他数量的NA
值下都可以正常运行。
我检查了软件包作者,这似乎是一个错误。在修复之前,您可以为那些具有单个NA
值的列选择其他插补方法。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,但是如果您设置为使用randomforest方法,则以上内容为您提供了一个选择。
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我来说两句