zoukankan      html  css  js  c++  java
  • pearson相关分析在R中的实现

    三个相关性函数:

    cor():R自带的,输入数据可以是vector,matrix,data.frame,输出两两的相关系数R值

    cor.test():R自带的,输入数据只能是两个vector,输出两个变量的相关系数R值,显著性水平a值

    corr.test():psych包的,输入数据可以是data.frame,输出两两变量的相关系数R值,显著性水平a值

    > cor(state.x77)
                Population     Income  Illiteracy    Life Exp     Murder     HS Grad      Frost        Area
    Population  1.00000000  0.2082276  0.10762237 -0.06805195  0.3436428 -0.09848975 -0.3321525  0.02254384
    Income      0.20822756  1.0000000 -0.43707519  0.34025534 -0.2300776  0.61993232  0.2262822  0.36331544
    Illiteracy  0.10762237 -0.4370752  1.00000000 -0.58847793  0.7029752 -0.65718861 -0.6719470  0.07726113
    Life Exp   -0.06805195  0.3402553 -0.58847793  1.00000000 -0.7808458  0.58221620  0.2620680 -0.10733194
    Murder      0.34364275 -0.2300776  0.70297520 -0.78084575  1.0000000 -0.48797102 -0.5388834  0.22839021
    HS Grad    -0.09848975  0.6199323 -0.65718861  0.58221620 -0.4879710  1.00000000  0.3667797  0.33354187
    Frost      -0.33215245  0.2262822 -0.67194697  0.26206801 -0.5388834  0.36677970  1.0000000  0.05922910
    Area        0.02254384  0.3633154  0.07726113 -0.10733194  0.2283902  0.33354187  0.0592291  1.00000000
    > cor.test(state.x77[,1],state.x77[,2])
    
    	Pearson's product-moment correlation
    
    data:  state.x77[, 1] and state.x77[, 2]
    t = 1.475, df = 48, p-value = 0.1467
    alternative hypothesis: true correlation is not equal to 0
    95 percent confidence interval:
     -0.07443435  0.45991855
    sample estimates:
          cor 
    0.2082276 
    
    > corr.test(state.x77)
    Call:corr.test(x = state.x77)
    Correlation matrix 
               Population Income Illiteracy Life Exp Murder HS Grad Frost  Area
    Population       1.00   0.21       0.11    -0.07   0.34   -0.10 -0.33  0.02
    Income           0.21   1.00      -0.44     0.34  -0.23    0.62  0.23  0.36
    Illiteracy       0.11  -0.44       1.00    -0.59   0.70   -0.66 -0.67  0.08
    Life Exp        -0.07   0.34      -0.59     1.00  -0.78    0.58  0.26 -0.11
    Murder           0.34  -0.23       0.70    -0.78   1.00   -0.49 -0.54  0.23
    HS Grad         -0.10   0.62      -0.66     0.58  -0.49    1.00  0.37  0.33
    Frost           -0.33   0.23      -0.67     0.26  -0.54    0.37  1.00  0.06
    Area             0.02   0.36       0.08    -0.11   0.23    0.33  0.06  1.00
    Sample Size 
    [1] 50
    Probability values (Entries above the diagonal are adjusted for multiple tests.) 
               Population Income Illiteracy Life Exp Murder HS Grad Frost Area
    Population       0.00   1.00       1.00     1.00   0.23    1.00  0.25 1.00
    Income           0.15   0.00       0.03     0.23   1.00    0.00  1.00 0.16
    Illiteracy       0.46   0.00       0.00     0.00   0.00    0.00  0.00 1.00
    Life Exp         0.64   0.02       0.00     0.00   0.00    0.00  0.79 1.00
    Murder           0.01   0.11       0.00     0.00   0.00    0.01  0.00 1.00
    HS Grad          0.50   0.00       0.00     0.00   0.00    0.00  0.16 0.25
    Frost            0.02   0.11       0.00     0.07   0.00    0.01  0.00 1.00
    Area             0.88   0.01       0.59     0.46   0.11    0.02  0.68 0.00
    
     To see confidence intervals of the correlations, print with the short=FALSE option
    

      

  • 相关阅读:
    线程数量与并行应用性能相关性的测试
    redis命令学习
    shell获取日期(昨天,明天,上月,下月)
    shell获取文件行数
    redis的备份和恢复
    redis使用Java学习
    kafka的一些常用命令
    查看kafka的group.id
    vim搜索后跳到下(上)一个
    redis批量执行
  • 原文地址:https://www.cnblogs.com/timeisbiggestboss/p/8477138.html
Copyright © 2011-2022 走看看