我在 R 上创建了一个逻辑回归模型,问题是我的最大 x 值为 0.85,因此图形只能画到这个值。
有没有办法将此图形扩展到 x=100 并使用我的逻辑回归模型计算 y 值呢?
library(caret)
library(mlbench)
library(ggplot2)
library(tidyr)
library(caTools)
my_data2 <- read.csv('C:/Users/Magician/Desktop/R files/Fnaticfirstround.csv', header=TRUE, stringsAsFactors = FALSE)
my_data2
#converting Map names to the calculated win probability
my_data2[my_data2$Map == "Dust2", "Map"] <- 0.307692
my_data2[my_data2$Map == "Inferno", "Map"] <- 0.47619
my_data2[my_data2$Map == "Mirage", "Map"] <- 0.708333
my_data2[my_data2$Map == "Nuke", "Map"] <- 0.444444
my_data2[my_data2$Map == "Overpass", "Map"] <- 0.333333
my_data2[my_data2$Map == "Train", "Map"] <- 0.692308
my_data2[my_data2$Map == "Vertigo", "Map"] <- 0
my_data2[my_data2$Map == "Cache", "Map"] <- 0.857143
#converting W and L to 1 and 0
my_data2$WinorLoss <- ifelse(my_data2$WinorLoss == "W", 1,0)
my_data2$WinorLoss <- factor(my_data2$WinorLoss, levels = c(0,1))
#converting Map to numeric characters
my_data2$Map <- as.numeric(my_data2$Map)
#Logistic regression model
glm.fit <- glm(WinorLoss ~ Map, family=binomial, data=my_data2)
summary(glm.fit)
#make predictions on the training data
glm.probs <- predict(glm.fit, type="response")
glm.pred <- ifelse(glm.probs>0.5, 1, 0)
attach(my_data2)
table(glm.pred,WinorLoss)
mean(glm.pred==WinorLoss)
#splitting the data for trying and testing
Split <- sample.split(my_data2, SplitRatio = 0.7)
traindata <- subset(my_data2, Split == "TRUE")
testdata <- subset(my_data2, Split == "FALSE")
glm.fit <- glm(WinorLoss ~ Map,
data=traindata,
family="binomial")
glm.probs <- predict(glm.fit,
newdata=testdata,
type="response")
glm.pred <- ifelse(glm.probs > 0.5, "1", "0")
table(glm.pred, testdata$WinorLoss)
mean(glm.pred == testdata$WinorLoss)
summary(glm.fit)
#changing the x axis to 0-100%, min map win prob - max map win prob
newdat <- data.frame(Map = seq(min(traindata$Map), max(traindata$Map), len=100))
newdat$WinorLoss = predict(glm.fit, newdata=newdat, type="response")
p <- ggplot(newdat, aes(x=Map,y=WinorLoss))+
geom_point() +
geom_smooth(method = "glm",
method.args = list(family="binomial"),
se = FALSE) +
xlim(0,1) +
ylim(0,1)
我尝试将x值扩展到100,但这只是扩展了坐标轴而没有计算相应的y值,因此无法绘制这些值。
geom_smooth(fullrange = TRUE)
- cuttlefish44