如何计算R中的类内相关系数？

在本文中，我们将讨论如何在 R 编程语言中计算类内相关系数。相关性用于获得两个变量之间的关系。

• 如果值为1，则关系为正相关
• 如果值为-1，则关系为负相关
• 如果值为0，则关系没有相关性

类内相关系数用于确定元素是否可以被不同的评分者可靠地评分。范围从 0 开始，以 1 结束。

我们可以使用 icc() 方法，它在 irr 包中可用，代表 interrater 可靠性

Syntax: icc(data, model, type, unit)

where,

• data is the input dataframe
• model is the type of model to use. there are two types oneway and twoway.
• type is the  relationship to calculate between raters. Options include “consistency” or “agreement”
• unit is for analysis , which is a single or average

Return: It will return the following:

• model – The type of model – oneway or twoway
• type – The type of model which is agreement
• Subjects- Total number of data in each column of the dataframe
• Raters – The number of columns of the dataframe
• ICC value – Intra correlation coefficient value
• Ftest –  F value and P value
• CI – Confidence Interval of the Correlated values.

示例 1：计算单向模型的 icc

在此示例中，我们将创建一个具有 4 列的数据框，并为具有单个单元的单向模型计算 icc

R

# load the library
library(irr)
  
# create dataframe with 4 columns
data = data.frame(col1=c(1:10), col2=c(34:43),
                  col3=c(20:29), col4=c(56:65))
  
# calculate icc for oneway model
icc(data, model = "oneway",
    type = "agreement", 
    unit = "single")

输出：

In this output, As mentioned in the code , The model is oneway and Type is agreement

As there are four columns columns in the dataframe , so the Raters=4

In each column, the number of rows are 10 , so the Subjects = 10

The Intra correlation Coefficient (ICC) is -0.304

The F-test value we got is 0,0692 and the p value is 1

Finally, we got the 95% Confidence Interval level is in between -0.332 to -0.233

示例 2：计算双向模型的 icc

在此示例中，我们将创建一个具有 4 列的数据框，并为具有单个单元的双向模型计算 icc。

R

# load the library
library(irr)
  
# create dataframe with 4 columns
data = data.frame(col1 = c(1:10), 
                  col2 = c(34:43),
                  col3 = c(20:29),
                  col4 = c(56:65))
  
# calculate icc for twoway model
icc(data, model = "twoway",
    type = "agreement", 
    unit = "single")

输出：

In this output, As mentioned in the code, The model is two-way and Type is agreement

As there are four columns in the dataframe , so the Raters=4

In each column, the number of rows are 10 , so the Subjects = 10

The Intra correlation Coefficient (ICC) is -0.0168

The F-test value we got is -5.44 exponent and the p value is 1

Finally, we got the 95% Confidence Interval level is in between 0.001 to 0.00

示例 3：计算平均单位的 icc

在此示例中，我们将创建一个具有 4 列的数据框，并为具有平均单位的双向模型计算 icc。

R

# load the library
library(irr)
  
# create dataframe with 4 columns
data = data.frame(col1 = c(1:10),
                  col2 = c(34:43),
                  col3 = c(20:29),
                  col4 = c(56:65))
  
# calculate icc for twoway model with average unit
icc(data, model = "twoway", type = "agreement",
    unit = "average")

输出：

In this output, As mentioned in the code, The model is oneway and Type is agreement

As there are four columns in the dataframe , so the Raters=4

In each column, the number of rows are 10 , so the Subjects = 10

The Intra correlation Coefficient (ICC) is 0.0639

The F-test value we got is -5.44 exp and the p value is 1

Finally, we got the 95% Confidence Interval level is in between 0.005 to 0.257

示例 4：计算单个单元的 icc

在此示例中，我们将创建一个具有 4 列的数据框，并为具有单个单元的双向模型计算 icc

R

# load the library
library(irr)
  
# create dataframe with 4 columns
data = data.frame(col1 = c(1:10),
                  col2 = c(34:43), 
                  col3 = c(20:29),
                  col4 = c(56:65))
  
# calculate icc for twoway
# model with single unit
icc(data, model = "twoway", type = "agreement",
    unit = "single")

输出：

In this output, As mentioned in the code, The model is twoway and Type is an agreement.

As there are four columns in the dataframe, so the Raters=4

In each column, the number of rows are 10, so the Subjects = 10

The Intra Correlation Coefficient (ICC) is 0.0168

The F-test value we got is -5.44 exp and the p value is 1

Finally, we got the 95% Confidence Interval level is in between 0.001 to 0.00