我为this answer中的混淆矩阵改编了热图图。但是我想扭转它。在对角线(从左上方到右下方)是匹配项(正确的分类)。我的目标是将对角线绘制成黄色调色板。并且在红色调色板中不匹配(因此,除了对角线以外的所有图块)。
在我的plot.cm
函数中,我可以使用对角线
cm_d$diag <- cm_d$Prediction == cm_d$Reference # Get the Diagonal
cm_d$ndiag <- cm_d$Prediction != cm_d$Reference # Not the Diagonal
并且使用正确的geom_tile
美学,我只能得到对角线(在所需的淡黄色状态下)
geom_tile( data = cm_d[!is.na(cm_d$diag), ],aes(color = Freq)) +
scale_fill_gradient(guide = FALSE,low=alpha("lightyellow",0.75), high="yellow",na.value = 'white')
但是我无法在cm_d$ndiag
的元素上获得第二种配色方案我找到了提供ggnewscale和new_scale()
的软件包new_scale_fill()
。我很累在blog的帮助下实现它。但是,对于其余的热图,结果仅是深灰色填充的图块
# adapted from https://stackoverflow.com/a/60150826/7318488
library(ggplot2) # to plot
library(gridExtra) # to put more
library(grid) # plot together
library(likert) # for reversing the factor order
library(ggnewscale)
plot.cm <- function(cm){
# extract the confusion matrix values as data.frame
cm_d <- as.data.frame(cm$table)
cm_d$diag <- cm_d$Prediction == cm_d$Reference # Get the Diagonal
cm_d$ndiag <- cm_d$Prediction != cm_d$Reference # Not the Diagonal
cm_d[cm_d == 0] <- NA # Replace 0 with NA for white tiles
cm_d$Reference <- reverse.levels(cm_d$Reference) # diagonal starts at top left
# plotting the matrix
cm_d_p <- ggplot(data = cm_d, aes(x = Prediction , y = Reference, fill = Freq))+
scale_x_discrete(position = "top") +
geom_tile( data = cm_d[!is.na(cm_d$diag), ],aes(color = Freq)) +
scale_fill_gradient(guide = FALSE,low=alpha("lightyellow",0.75), high="yellow",na.value = 'white') +
# THIS DOESNT WORK
# new_scale("fill") +
# geom_tile( data = cm_d[!is.na(cm_d$ndiag), ],aes(color = Freq)) +
# scale_fill_gradient(guide = FALSE,low=alpha("red",0.75), high="darkred",na.value = 'white') +
geom_text(aes(label = Freq), color = 'black', size = 6) +
theme_light() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
legend.position = "none",
panel.border = element_blank(),
plot.background = element_blank(),
axis.line = element_blank())
return(cm_d_p)
}
样本数据:模拟插入符混淆矩阵
library(caret)
# simulated data
set.seed(23)
pred <- factor(sample(1:7,100,replace=T))
ref<- factor(sample(1:7,100,replace=T))
cm <- caret::confusionMatrix(pred,ref)
g <- plot.cm(cm)
g
我相信问题很简单,您是在指定aes(color = Freq)
而不是aes(fill = Freq
。情节是您的目标吗?您还可以通过仅使用发散的色标并创建一个新的变量(如果它偏离对角线将Freq标记为负)来简化所有这些操作?请参阅下面的第二个示例
# adapted from https://stackoverflow.com/a/60150826/7318488
library(ggplot2) # to plot
library(gridExtra) # to put more
library(grid) # plot together
library(likert) # for reversing the factor order
#> Loading required package: xtable
library(ggnewscale)
plot.cm <- function(cm){
# extract the confusion matrix values as data.frame
cm_d <- as.data.frame(cm$table)
cm_d$diag <- cm_d$Prediction == cm_d$Reference # Get the Diagonal
cm_d$ndiag <- cm_d$Prediction != cm_d$Reference # Not the Diagonal
cm_d[cm_d == 0] <- NA # Replace 0 with NA for white tiles
cm_d$Reference <- reverse.levels(cm_d$Reference) # diagonal starts at top left
# plotting the matrix
cm_d_p <- ggplot(data = cm_d, aes(x = Prediction , y = Reference, fill = Freq))+
scale_x_discrete(position = "top") +
geom_tile( data = cm_d[!is.na(cm_d$diag), ],aes(fill = Freq)) +
scale_fill_gradient(guide = FALSE,low=alpha("lightyellow",0.75), high="yellow",na.value = 'white') +
# THIS DOESNT WORK
new_scale("fill") +
geom_tile( data = cm_d[!is.na(cm_d$ndiag), ],aes(fill = Freq)) +
scale_fill_gradient(guide = FALSE,low=alpha("red",0.75), high="red",na.value = 'white') +
geom_text(aes(label = Freq), color = 'black', size = 6) +
theme_light() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
legend.position = "none",
panel.border = element_blank(),
plot.background = element_blank(),
axis.line = element_blank())
return(cm_d_p)
}
library(caret)
#> Loading required package: lattice
# simulated data
set.seed(23)
pred <- factor(sample(1:7,100,replace=T))
ref<- factor(sample(1:7,100,replace=T))
cm <- caret::confusionMatrix(pred,ref)
g <- plot.cm(cm)
g
#> Warning: Removed 8 rows containing missing values (geom_text).
<< img src =“ https://image.soinside.com/eyJ1cmwiOiAiaHR0cHM6Ly9pLmltZ3VyLmNvbS91RzEzUER3LnBuZyJ9” alt =“”>
由reprex package(v0.3.0)在2020-04-29创建
# adapted from https://stackoverflow.com/a/60150826/7318488
library(ggplot2) # to plot
library(gridExtra) # to put more
library(grid) # plot together
library(likert) # for reversing the factor order
#> Loading required package: xtable
library(ggnewscale)
plot.cm <- function(cm){
# extract the confusion matrix values as data.frame
cm_d <- as.data.frame(cm$table)
cm_d$diag <- cm_d$Prediction == cm_d$Reference # Get the Diagonal
cm_d$ndiag <- cm_d$Prediction != cm_d$Reference # Not the Diagonal
cm_d[cm_d == 0] <- NA # Replace 0 with NA for white tiles
cm_d$Reference <- reverse.levels(cm_d$Reference) # diagonal starts at top left
cm_d$ref_freq <- cm_d$Freq * ifelse(is.na(cm_d$diag),-1,1)
# plotting the matrix
cm_d_p <- ggplot(data = cm_d, aes(x = Prediction , y = Reference, fill = Freq))+
scale_x_discrete(position = "top") +
geom_tile( data = cm_d,aes(fill = ref_freq)) +
scale_fill_gradient2(guide = FALSE,low="red",high="yellow", midpoint = 0,na.value = 'white') +
geom_text(aes(label = Freq), color = 'black', size = 6)+
theme_light() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
legend.position = "none",
panel.border = element_blank(),
plot.background = element_blank(),
axis.line = element_blank())
return(cm_d_p)
}
library(caret)
#> Loading required package: lattice
# simulated data
set.seed(23)
pred <- factor(sample(1:7,100,replace=T))
ref<- factor(sample(1:7,100,replace=T))
cm <- caret::confusionMatrix(pred,ref)
g <- plot.cm(cm)
g
#> Warning: Removed 8 rows containing missing values (geom_text).
由reprex package(v0.3.0)在2020-04-29创建