如何在 ggplot2 中绘制 logit 和 probit [英] How to plot logit and probit in ggplot2
问题描述
这几乎肯定是一个新手问题/
This is almost surely a newbish question/
对于下面的数据集，我一直试图在 ggplot2 中绘制 logit 和 probit 曲线，但没有成功.
For the dataset below I have been trying to plot both the logit and the probit curves in ggplot2 without success.
Ft Temp TD
1 66 0
6 72 0
11 70 1
16 75 0
21 75 1
2 70 1
7 73 0
12 78 0
17 70 0
22 76 0
3 69 0
8 70 0
13 67 0
18 81 0
23 58 1
4 68 0
9 57 1
14 53 1
19 76 0
5 67 0
10 63 1
15 67 0
20 79 0
我天真地使用的代码是
library(ggplot2)
TD<-mydata$TD
Temp<-mydata$Temp
g<- qplot(Temp,TD)+geom_point()+stat_smooth(method="glm",family="binomial",formula=y~x,col="red")
g1<-g+labs(x="Temperature",y="Thermal Distress")
g1
g2<-g1+stat_smooth(method="glm",family="binomial",link="probit",formula=y~x,add=T)
g2
你能告诉我如何改进我的代码以便在同一张图上绘制这两条曲线吗?
Could you please tell me how I could improve my code so as to plot these two curves on the same graph?
谢谢
推荐答案
另一种方法是生成您自己的预测值并使用 ggplot 绘制它们——然后您可以更好地控制最终的绘图(而不是依赖于 stat_smooth 用于计算；如果您使用多个协变量并且在绘图时需要在它们的均值或模式上保持一些常数，这尤其有用.
An alternative approach would be to generate your own predicted values and plot them with ggplot—then you can have more control over the final plot (rather than relying on stat_smooth for the calculations; this is especially useful if you're using multiple covariates and need to hold some constant at their means or modes when plotting).
library(ggplot2)
# Generate data
mydata <- data.frame(Ft = c(1, 6, 11, 16, 21, 2, 7, 12, 17, 22, 3, 8,
13, 18, 23, 4, 9, 14, 19, 5, 10, 15, 20),
Temp = c(66, 72, 70, 75, 75, 70, 73, 78, 70, 76, 69, 70,
67, 81, 58, 68, 57, 53, 76, 67, 63, 67, 79),
TD = c(0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0))
# Run logistic regression model
model <- glm(TD ~ Temp, data=mydata, family=binomial(link="logit"))
# Create a temporary data frame of hypothetical values
temp.data <- data.frame(Temp = seq(53, 81, 0.5))
# Predict the fitted values given the model and hypothetical data
predicted.data <- as.data.frame(predict(model, newdata = temp.data,
type="link", se=TRUE))
# Combine the hypothetical data and predicted values
new.data <- cbind(temp.data, predicted.data)
# Calculate confidence intervals
std <- qnorm(0.95 / 2 + 0.5)
new.data$ymin <- model$family$linkinv(new.data$fit - std * new.data$se)
new.data$ymax <- model$family$linkinv(new.data$fit + std * new.data$se)
new.data$fit <- model$family$linkinv(new.data$fit) # Rescale to 0-1
# Plot everything
p <- ggplot(mydata, aes(x=Temp, y=TD))
p + geom_point() +
geom_ribbon(data=new.data, aes(y=fit, ymin=ymin, ymax=ymax), alpha=0.5) +
geom_line(data=new.data, aes(y=fit)) +
labs(x="Temperature", y="Thermal Distress")
奖励，只是为了好玩:如果您使用自己的预测函数，您可以对协变量感到疯狂，例如展示模型如何在 Ft 的不同级别上拟合:
Bonus, just for fun: If you use your own prediction function, you can go crazy with covariates, like showing how the model fits at different levels of Ft:
# Alternative, if you want to go crazy
# Run logistic regression model with two covariates
model <- glm(TD ~ Temp + Ft, data=mydata, family=binomial(link="logit"))
# Create a temporary data frame of hypothetical values
temp.data <- data.frame(Temp = rep(seq(53, 81, 0.5), 2),
Ft = c(rep(3, 57), rep(18, 57)))
# Predict the fitted values given the model and hypothetical data
predicted.data <- as.data.frame(predict(model, newdata = temp.data,
type="link", se=TRUE))
# Combine the hypothetical data and predicted values
new.data <- cbind(temp.data, predicted.data)
# Calculate confidence intervals
std <- qnorm(0.95 / 2 + 0.5)
new.data$ymin <- model$family$linkinv(new.data$fit - std * new.data$se)
new.data$ymax <- model$family$linkinv(new.data$fit + std * new.data$se)
new.data$fit <- model$family$linkinv(new.data$fit) # Rescale to 0-1
# Plot everything
p <- ggplot(mydata, aes(x=Temp, y=TD))
p + geom_point() +
geom_ribbon(data=new.data, aes(y=fit, ymin=ymin, ymax=ymax,
fill=as.factor(Ft)), alpha=0.5) +
geom_line(data=new.data, aes(y=fit, colour=as.factor(Ft))) +
labs(x="Temperature", y="Thermal Distress")
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