我如何拉取一个基于组的向量来传递给dplyr总结或变异中的一个函数？
我如何拉取一个基于组的向量来传递给dplyr总结或变异中的一个函数？
提问于 2019-03-08 06:40:37
我正在尝试使用psych包中的AUC函数创建一个关于准确性、敏感度和特异性的汇总表。我想为分组变量的每个级别定义输入向量(t，一个4x1的向量)。
我所尝试的似乎忽略了分组。
示例：
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))$Specificity)这使我接近正确的输出，除了准确性、敏感度和特异度的值只在第一行计算，然后重复计算：
# A tibble: 4 x 8
# Groups: Class [4]
Class TP FP FN TN Accuracy Sensitivity Specificity
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A 198 1 42 569 0.947 0.995 0.931
2 B 185 0 55 570 0.947 0.995 0.931
3 C 221 6 19 564 0.947 0.995 0.931
4 D 192 1 48 569 0.947 0.995 0.931我也尝试过使用summarize
Data %>%
group_by(Class) %>%
summarize(Accuracy = AUC(t = unlist(.[1,2:5], use.names=FALSE))$Accuracy,
Sensitivity = AUC(t = unlist(.[1,2:5], use.names=FALSE))$Sensitivity,
Specificity = AUC(t = unlist(.[1,2:5], use.names=FALSE))$Specificity)但输出结果与上面相同。
所需的输出是每个"Class“级别的唯一计算
# 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我如何在summarize或mutate中调用函数来维护组？
回答 2
Stack Overflow用户
回答已采纳
发布于 2019-03-08 07:14:54
这是可行的
Data %>%
group_by(Class) %>%
mutate(Accuracy = AUC(t = unlist(.[Class,2:5], use.names=FALSE))$Accuracy,
Sensitivity = AUC(t = unlist(.[Class,2:5], use.names=FALSE))$Sensitivity,
Specificity = AUC(t = unlist(.[Class,2:5], use.names=FALSE))$Specificity)但也许这一点更清楚
Data %>%
group_by(Class) %>%
mutate(Accuracy = AUC(t = c(TP, FP, FN, TN))$Accuracy,
Sensitivity = AUC(t = c(TP, FP, FN, TN))$Sensitivity,
Specificity = AUC(t = c(TP, FP, FN, TN))$Specificity)Stack Overflow用户
发布于 2019-03-08 07:22:19
为了避免为每个类多次调用AUC，我编写了一个包装器，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页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/55053971
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