如何将 Gamma 分布拟合到 R 中的数据集

Gamma 分布专门用于确定指数分布、Erlang 分布和卡方分布。它也被称为具有连续概率分布的二参数族。

逐步实施

步骤 1：在 R 中安装并导入 fitdistrplus 包：

install.package("fitdistrplus")
library(fitdistrplus)

fitdistrplus 包为我们提供了 fitdist函数来拟合分布。

句法：

fitdist(dataset, distr = “choice”, method = “method”)

Here,

• distr = “choice” : It represents the distribution choice
• method = “method” : It represents the method of fitting the data

第 2 步：现在，我们将借助伽马分布拟合数据集数据，并借助最大似然估计方法拟合数据集。

R

# Generating 20 random values that uses 
# a gamma distribution having shape 
# parameter as 10
# combined with some gaussian noise
data <- rgamma(20, 3, 10) + rnorm(20, 0, .02)
  
# Fit the dataset to a gamma distribution
# using mle
ans <- fitdist(data, distr = "gamma", method = "mle")
  
# Display the summary of ans
summary(ans)

输出：

现在我们将生成一些图，这些图将在以下语法的帮助下显示 gamma 分布与数据集的拟合程度。

R

# Import the package
library(fitdistrplus)
  
# Generating 20 random values that uses a
# gamma distribution having shape parameter 
# as 10
# combined with some gaussian noise
data <- rgamma(20, 3, 10) + rnorm(20, 0, .02)
  
# Fitting the dataset to a gamma distribution
# with the help of mle
ans <- fitdist(data, distr = "gamma", method = "mle")
  
# Display the plot 
plot(ans)

输出：

例子：

R

# Import the package
library(fitdistrplus)
  
# Generating 20 random values that uses a 
# gamma distribution having shape parameter
# as 10
# combined with some gaussian noise
data <- rgamma(20, 3, 10) + rnorm(20, 0, .02)
  
# Fitting the dataset to a gamma distribution 
# with the help of mle
ans <- fitdist(data, distr = "gamma", method = "mle")
  
# Display the summary of the ans
summary(ans)
  
# Display the plot
plot(ans)

输出：