概率密度函数可视化-Toy模板网

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概率密度函数可视化

1 一维随机变量情形

以正态概率密度函数为例，其中位置参数为 $\mu$ ，尺度参数为 $\sigma$ ，
$\dfrac{1}{\sqrt{2\pi}\sigma}e^{-\dfrac{(x-\mu)^2}{2\sigma^2}},x\in R$

library(tidyverse)
# 一维情形
set.seed(1)
N <- 10000
data <- data.frame(v1 = rnorm(N, 1, 1))
data %>% ggplot(aes(x = v1)) +
  geom_density(colour = 2, size = 1.5, fill = "#009933") +
  xlab("X") +
  ylab("Density") +
  theme_bw() +
  theme(axis.title.x = element_text(vjust = 1, size = 15)) +
  theme(axis.title.y = element_text(vjust = 1, size = 15)) +
  theme(axis.text.x = element_text(vjust = 1, size = 15)) +
  theme(axis.text.y = element_text(vjust = 1, size = 15)) +
  annotate("text",
    x = 3, y = 0.3, parse = TRUE, size = 6,
    label = "y==frac(1, sqrt(2*pi)) * e^{frac(-(x-1)^2 , 2)}"
  )

概率密度函数可视化

2 二维随机变量情形

以多元正态概率密度函数为例，随机变量 $X_1\dots X_n$ 的联合正态概率密度函数为
$p(x)=p\left(x_{1}, x_{2}, \ldots, x_{n}\right)=\frac{1}{(2 \pi)^{n / 2}|\Sigma|^{1 / 2}} \exp \left(-\frac{1}{2}(x-\mu)^{T} \Sigma^{-1}(x-\mu)\right)$
其中 $\Sigma$ 为随机变量 $X_1\dots X_n$ 的方差协方差矩阵， $\mu$ 为各随机变量均值向量。当 $n = 2$ 时，概率密度函数为
$\frac{1}{2 \pi \sigma_{1} \sigma_{2} \sqrt{1-\rho^{2}}} \exp \left(-\frac{1}{2} \frac{\left(\frac{x_{1}-\mu_{1}}{\sigma_{1}}\right)^{2}-2 \rho\left(\frac{x_{1}-\mu_{1}}{\sigma_{1}}\right)\left(\frac{x_{2}-\mu_{2}}{\sigma_{2}}\right)+\left(\frac{x_{2}-\mu_{2}}{\sigma_{2}}\right)^{2}}{1-\rho^{2}}\right)$
其中 $\rho$ 表示 $x_1$ 和 $x_2$ 的相关系数。

# 二维情形
rm(list = ls())
n <- 10000
mean <- c(-1, 1)
sigma <- matrix(c(1, 0.7, 0.7, 1), nrow = 2, ncol = 2)
mydata <- as.data.frame(MASS::mvrnorm(n, mean, sigma))

# 二维变量散点图
mydata %>% ggplot( aes(x = V1, y = V2)) +
  geom_bin2d(bins = 80) +
  scale_fill_viridis_c(option = "D") +
  theme_bw()+
  guides(fill = guide_colorbar(title = "频数"))+
  theme(legend.position = c(0.9,0.2), legend.title = element_text(size = 15))

概率密度函数可视化

# 二维随机变量联合密度
gg <- mydata %>%  ggplot( aes(x = V1, y = V2)) +
  stat_density_2d(aes(fill = stat(nlevel)), geom = "polygon", colour = "white") +
  # 轴标签
  xlab("随机变量1") +
  ylab("随机变量2") +
  # 背景颜色
  theme_bw() +
  # 填充颜色设置
  scale_fill_viridis_c(option = "D") +
  # x轴标签设置
  theme(axis.title.x = element_text(vjust = -1, size = 30)) +
  # y轴标签设置
  theme(axis.title.y = element_text(vjust = 1, size = 30)) +
  # x轴刻度尺标签设置
  theme(axis.text.x = element_text(vjust = 1, size = 30)) +
  # y轴刻度尺标签设置
  theme(axis.text.y = element_text(vjust = 1, size = 30)) +
  # 图例设置
  guides(fill = guide_colorbar(title = " ")) +
  theme(legend.position = c(0.15, 0.8), legend.title = element_text(size = 15))
rayshader::plot_gg(gg, multicore = TRUE, width = 10, height = 10, scale = 1000)
rgl:rgl.postscript("rgl1.svg", fmt = "svg",drawText = TRUE)

概率密度函数可视化

u1 = 0
u2 = 0
sigma1 = 1
sigma2 = 1
rho = 0.7

f <- function(X,Y){
  return( 1/(2*pi*sigma1*sigma2*sqrt(1-rho^2))*exp(-1/(2*sqrt(1-rho^2))*(X^2+Y^2-2*rho*X*Y)))
}
X = seq(-4,4,0.1)
Y = seq(-4,4,0.1)
Z = outer(X,Y,f)
library(shape)
persp(X,Y,Z,theta = -40,phi=20,expand = 0.6,r = 10,shade = 0.01,
      ticktype = "detailed",xlab = "X",ylab = "Y",zlab = expression(f(X,Y)),
      col =  drapecol(Z))

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