我正在尝试使用包中的AUC函数创建准确性，灵敏度和特异性的汇总表psych。我想为分组变量的每个级别定义输入向量（t，4 x 1向量）。

我试过的似乎忽略了分组。

例：

library(tidyverse)

library(psych)

Data <- data.frame(Class = c("A","B","C","D"),

TP = c(198,185,221,192),

FP = c(1,1,6,1),

FN = c(42,55,19,48),

TN = c(569,570,564,569))

Data %>%

group_by(Class) %>%

mutate(Accuracy = AUC(t = unlist(.[1,2:5], use.names=FALSE))$Accuracy,

Sensitivity = AUC(t = unlist(.[1,2:5], use.names=FALSE))$Sensitivity,

Specificity = AUC(t = unlist(.[1,2:5], use.names=FALSE))$Specificity)

这使我接近正确的输出，除了精度，灵敏度和特异性的值仅用第一行计算，然后重复：

# A tibble: 4 x 8

# Groups: Class [4]

Class TP FP FN TN Accuracy Sensitivity Specificity

<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 A 198 1 42 569 0.947 0.995 0.931

2 B 185 0 55 570 0.947 0.995 0.931

3 C 221 6 19 564 0.947 0.995 0.931

4 D 192 1 48 569 0.947 0.995 0.931

我也尝试过summarize：

Data %>%

group_by(Class) %>%

summarize(Accuracy = AUC(t = unlist(.[1,2:5], use.names=FALSE))$Accuracy,

Sensitivity = AUC(t = unlist(.[1,2:5], use.names=FALSE))$Sensitivity,

Specificity = AUC(t = unlist(.[1,2:5], use.names=FALSE))$Specificity)

但输出与上面相同。

所需的输出是每个级别“Class”的唯一计算

# A tibble: 4 x 8

Class TP FP FN TN Accuracy Sensitivity Specificity

<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>

1 A 198 1 42 569 0.95 0.99 0.93

2 B 185 0 55 570 0.93 0.99 0.91

3 C 221 6 19 564 0.97 0.97 0.97

4 D 192 1 48 569 0.94 0.99 0.92

如何在汇总或变异中获取函数调用以维护组？

解决办法：为了避免AUC为每个类调用几次，我会写一个包装器，如下所示：

# Load libraries

library(tidyverse)

library(psych)

# Create data frame

Data <- data.frame(Class = c("A","B","C","D"),

TP = c(198,185,221,192),

FP = c(1,1,6,1),

FN = c(42,55,19,48),

TN = c(569,570,564,569))

# Wrapper function

AUC_wrapper <- function(Class, TP, FP, FN, TN){

res <- AUC(t = c(TP, FP, FN, TN))

data.frame(Class = Class,

TP = TP,

FP = FP,

FN = FN,

TN = TN,

Accuracy = res$Accuracy,

Sensitivity = res$Sensitivity,

Specificity = res$Specificity)

}

# Run using purrr

pmap_dfr(Data, AUC_wrapper)

# Class TP FP FN TN Accuracy Sensitivity Specificity

# 1 A 198 1 42 569 0.9469136 0.9949749 0.9312602

# 2 B 185 1 55 570 0.9309494 0.9946237 0.9120000

# 3 C 221 6 19 564 0.9691358 0.9735683 0.9674099

# 4 D 192 1 48 569 0.9395062 0.9948187 0.9222042