我对此进行了广泛的研究,但没有找到解决方案。我已经清理了我的数据集,如下所示:
library("raster")
impute.mean <- function(x) replace(x, is.na(x) | is.nan(x) | is.infinite(x) ,
mean(x, na.rm = TRUE))
losses <- apply(losses, 2, impute.mean)
colSums(is.na(losses))
isinf <- function(x) (NA <- is.infinite(x))
infout <- apply(losses, 2, is.infinite)
colSums(infout)
isnan <- function(x) (NA <- is.nan(x))
nanout <- apply(losses, 2, is.nan)
colSums(nanout)
运行预测算法时出现问题:
options(warn=2)
p <- predict(default.rf, losses, type="prob", inf.rm = TRUE, na.rm=TRUE, nan.rm=TRUE)
所有研究都表明数据中应该是 NA、Inf 或 NaN,但我没有找到。我正在将数据和 randomForest 摘要提供给 [已删除] 进行调查 回溯并没有透露太多信息(无论如何对我来说):
4: .C("classForest", mdim = as.integer(mdim), ntest = as.integer(ntest),
nclass = as.integer(object$forest$nclass), maxcat = as.integer(maxcat),
nrnodes = as.integer(nrnodes), jbt = as.integer(ntree), xts = as.double(x),
xbestsplit = as.double(object$forest$xbestsplit), pid = object$forest$pid,
cutoff = as.double(cutoff), countts = as.double(countts),
treemap = as.integer(aperm(object$forest$treemap, c(2, 1,
3))), nodestatus = as.integer(object$forest$nodestatus),
cat = as.integer(object$forest$ncat), nodepred = as.integer(object$forest$nodepred),
treepred = as.integer(treepred), jet = as.integer(numeric(ntest)),
bestvar = as.integer(object$forest$bestvar), nodexts = as.integer(nodexts),
ndbigtree = as.integer(object$forest$ndbigtree), predict.all = as.integer(predict.all),
prox = as.integer(proximity), proxmatrix = as.double(proxmatrix),
nodes = as.integer(nodes), DUP = FALSE, PACKAGE = "randomForest")
3: predict.randomForest(default.rf, losses, type = "prob", inf.rm = TRUE,
na.rm = TRUE, nan.rm = TRUE)
2: predict(default.rf, losses, type = "prob", inf.rm = TRUE, na.rm = TRUE,
nan.rm = TRUE)
1: predict(default.rf, losses, type = "prob", inf.rm = TRUE, na.rm = TRUE,
nan.rm = TRUE)
最佳答案
您的代码并不完全可重现(没有运行实际的randomForest
算法),但您没有用平均值替换Inf
值列向量。这是因为 impute.mean
函数中调用 mean()
时的 na.rm = TRUE
参数的作用与它所说的完全一样 - - 删除 NA
值(而不是 Inf
值)。
例如,您可以通过以下方式查看:
impute.mean <- function(x) replace(x, is.na(x) | is.nan(x) | is.infinite(x), mean(x, na.rm = TRUE))
losses <- apply(losses, 2, impute.mean)
sum( apply( losses, 2, function(.) sum(is.infinite(.))) )
# [1] 696
要摆脱无限值,请使用:
impute.mean <- function(x) replace(x, is.na(x) | is.nan(x) | is.infinite(x), mean(x[!is.na(x) & !is.nan(x) & !is.infinite(x)]))
losses <- apply(losses, 2, impute.mean)
sum(apply( losses, 2, function(.) sum(is.infinite(.)) ))
# [1] 0
关于r - 如何消除 "NA/NaN/Inf in foreign function call (arg 7)"使用 randomForest 运行预测,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/21964078/