提问人：JTruant 提问时间：10/18/2023 更新时间：10/18/2023 访问量：21
R 代码警告：要替换的项目数不是替换长度的倍数
R Code Warning: number of items to replace is not a multiple of replacement length
问：
从鸢尾花数据集中，我创建了以下混淆矩阵：
在尝试使用一对多策略执行多类分类时，我提出了以下 R 代码：
mtrx <- matrix(c(10,0,0,0,10,1,0,0,9), ncol = 3)
rownames(mtrx) <- c("setosa","veriscolor", "virginica")
colnames(mtrx) <- c("setosa", "versicolor", "virginica")
cat <- colnames(mtrx)
for(n in 1:length(cat)) {
current <- cat[n]
binaryConMat <- mtrx
binaryConMat[binaryConMat != 0] <- ifelse(rownames(binaryConMat) == current, 1, 0)
print(binaryConMat)
TP <- binaryConMat[n, n]
FN <- sum(binaryConMat[n,]) - TP
FP <- sum(binaryConMat[,n]) - TP
TN <- sum(diag(binaryConMat)) - TP
sensitivity <- ifelse((TP + FN) == 0, 0, TP / (TP + FN))
specificity <- ifelse((TN + FP) == 0, 0, TN / (TN+FP))
precision <- ifelse((TP + FP) == 0, 0, TP / (TP + FP))
cat("Class:", current, "\n")
cat("Sensitivity:", round(sensitivity, 2), "\n")
cat("Specificity:", round(specificity, 2), "\n")
cat("Precision:", round(precision, 2), "\n")
cat("\n")
}
accuracy <- sum(diag(mtrx))/sum(mtrx)
cat("Overall Accuracy:",round(accuracy, 2), "\n")
但是，这会导致在运行代码时出现以下错误：
Warning: number of items to replace is not a multiple of replacement length
我已将问题隔离到以下代码行：
binaryConMat[binaryConMat != 0] <- ifelse(rownames(binaryConMat) == current, 1, 0)
但是，我不确定这个问题的合适解决方案是什么。我该怎么做才能解决此错误？
答：
1赞
Rui Barradas
10/18/2023
#1
这是更正的问题代码。
除了数据创建代码（我认为现在更简单）之外，更改都记录在注释中。
mtrx <- matrix(c(10,0,0,0,10,1,0,0,9), ncol = 3)
catg <- c("setosa","veriscolor", "virginica")
rownames(mtrx) <- catg
colnames(mtrx) <- catg
for(n in seq_along(catg)) {
current <- catg[n]
binaryConMat <- mtrx
# use the logical values directly since they alreay
# are FALSE/TRUE, meaning, 0/1
cond1 <- binaryConMat != 0
cond2 <- rownames(binaryConMat) == current
# the square brackets in binaryConMat[] are meant
# to preserve the shape of binaryConMat, a matrix
binaryConMat[] <- as.integer(cond1 & cond2)
print(binaryConMat)
TP <- binaryConMat[n, n]
FN <- sum(binaryConMat[n,]) - TP
FP <- sum(binaryConMat[,n]) - TP
TN <- sum(diag(binaryConMat)) - TP
sensitivity <- ifelse((TP + FN) == 0, 0, TP / (TP + FN))
specificity <- ifelse((TN + FP) == 0, 0, TN / (TN+FP))
precision <- ifelse((TP + FP) == 0, 0, TP / (TP + FP))
cat("Class:", current, "\n")
cat("Sensitivity:", round(sensitivity, 2), "\n")
cat("Specificity:", round(specificity, 2), "\n")
cat("Precision:", round(precision, 2), "\n")
cat("\n")
}
#> setosa veriscolor virginica
#> setosa 1 0 0
#> veriscolor 0 0 0
#> virginica 0 0 0
#> Class: setosa
#> Sensitivity: 1
#> Specificity: 0
#> Precision: 1
#>
#> setosa veriscolor virginica
#> setosa 0 0 0
#> veriscolor 0 1 0
#> virginica 0 0 0
#> Class: veriscolor
#> Sensitivity: 1
#> Specificity: 0
#> Precision: 1
#>
#> setosa veriscolor virginica
#> setosa 0 0 0
#> veriscolor 0 0 0
#> virginica 0 1 1
#> Class: virginica
#> Sensitivity: 0.5
#> Specificity: 0
#> Precision: 1
accuracy <- sum(diag(mtrx))/sum(mtrx)
cat("Overall Accuracy:",round(accuracy, 2), "\n")
#> Overall Accuracy: 0.97
创建于 2023-10-17 with reprex v2.0.2
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