将线段添加到密度图

发布于 2025-02-01 18:57:18 字数 608 浏览 2 评论 0原文

我想在图表中添加一条线，该行指示了平均值：

X1 <- rnorm(100)
# Kernel density estimates
density_X1 <- density(X1)
# Compute mode
mode_X1 <- density_X1$x[which.max(density_X1$y)]; mode_X1
# Compute mean
mean_X1 <- mean(X1)
# Create plot 
plot(density_X1, main = "Kernel density estimates", xlab = "X1", ylab = "density")
# Add line for mode
segments(x0 = mode_X1, y0 = 0, x1 = mode_X1, y1 = density_X1$y, col = "red", lty = 1, lwd = 1)
# Add line for mean
segments(x0 = mean_X1, y0 = 0, x1 = mean_X1, y1 = , col = "red", lty = 5, lwd = 2)

我需要输入段（y1 =）以确保上限是内核密度图的什么值？

原文

I would like to add a line to my plot that indicates the mean:

X1 <- rnorm(100)
# Kernel density estimates
density_X1 <- density(X1)
# Compute mode
mode_X1 <- density_X1$x[which.max(density_X1$y)]; mode_X1
# Compute mean
mean_X1 <- mean(X1)
# Create plot 
plot(density_X1, main = "Kernel density estimates", xlab = "X1", ylab = "density")
# Add line for mode
segments(x0 = mode_X1, y0 = 0, x1 = mode_X1, y1 = density_X1$y, col = "red", lty = 1, lwd = 1)
# Add line for mean
segments(x0 = mean_X1, y0 = 0, x1 = mean_X1, y1 = , col = "red", lty = 5, lwd = 2)

What value do I need to enter for segments(y1 = ) to ensure that the upper bound is the kernel density plot?

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他不在意 2025-02-08 18:57:18

要从离散点的密度估计值以某个X值计算Y值，您将需要进行某种插值。一种方法是使用大约的线性插值：

approx(x = density_X1$x, y = density_X1$y, xout = mean_X1)$y

其中，x和y给出要插值的点，xout 插值完成的X值的（向量）（请参阅of code>），

因此代码完全是：

set.seed(72405277)
X1 <- rnorm(100)
# Kernel density estimates
density_X1 <- density(X1)
# Compute mode
mdIdx <- which.max(density_X1$y) # store and resuse
mode_X1 <- density_X1$x[mdIdx]; 
# Compute mean
mean_X1 <- mean(X1)
# Create plot 
plot(density_X1, main = "Kernel density estimates", xlab = "X1", ylab = "density")
# Add line for mode
segments(x0 = mode_X1, y0 = 0, x1 = mode_X1, y1 = density_X1$y[mdIdx], col = "red", lty = 1, lwd = 1)
# Add line for mean
yax2 <- approx(x = density_X1$x, y = density_X1$y, xout = mean_X1)$y
segments(x0 = mean_X1, y0 = 0, x1 = mean_X1, y1 = yax2, col = "blue", lty = 5, lwd = 2)

To calculate the y-value at some x-value from a density estimate at discrete points, you will need to do some sort of interpolation. One way is to use linear interpolation with approx:

approx(x = density_X1$x, y = density_X1$y, xout = mean_X1)$y

where, x and y give the points to be interpolated, and xout the (vector of) x-values where the interpolation is done (See ?approx)

So altogether the code is:

set.seed(72405277)
X1 <- rnorm(100)
# Kernel density estimates
density_X1 <- density(X1)
# Compute mode
mdIdx <- which.max(density_X1$y) # store and resuse
mode_X1 <- density_X1$x[mdIdx]; 
# Compute mean
mean_X1 <- mean(X1)
# Create plot 
plot(density_X1, main = "Kernel density estimates", xlab = "X1", ylab = "density")
# Add line for mode
segments(x0 = mode_X1, y0 = 0, x1 = mode_X1, y1 = density_X1$y[mdIdx], col = "red", lty = 1, lwd = 1)
# Add line for mean
yax2 <- approx(x = density_X1$x, y = density_X1$y, xout = mean_X1)$y
segments(x0 = mean_X1, y0 = 0, x1 = mean_X1, y1 = yax2, col = "blue", lty = 5, lwd = 2)

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不必了 2025-02-08 18:57:18

您也可以使用Abline喜欢：

X1 <- rnorm(100)
# Kernel density estimates
density_X1 <- density(X1)
# Compute mode
mode_X1 <- density_X1$x[which.max(density_X1$y)]; mode_X1
# Compute mean
mean_X1 <- mean(X1)
# Create plot 
plot(density_X1, main = "Kernel density estimates", xlab = "X1", ylab = "density")
abline(v = mode_X1, col = "red", lty = 1, lwd = 1)
abline(v = mean_X1, col = "red", lty = 5, lwd = 2)

output：

You could also use abline like this:

X1 <- rnorm(100)
# Kernel density estimates
density_X1 <- density(X1)
# Compute mode
mode_X1 <- density_X1$x[which.max(density_X1$y)]; mode_X1
# Compute mean
mean_X1 <- mean(X1)
# Create plot 
plot(density_X1, main = "Kernel density estimates", xlab = "X1", ylab = "density")
abline(v = mode_X1, col = "red", lty = 1, lwd = 1)
abline(v = mean_X1, col = "red", lty = 5, lwd = 2)

Output: