江苏快三技巧讲座:用R进行路径分析

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示意图

0311.jpg

R脚本与注释

 江苏福彩快三下载安装 www.mhqs.net library(lavaan)                                                                 # 调用lavaan包,如果没有安装,需要先安装
curwd=setwd("c:\\program files\\mplus\\mplus examples\\user's guide examples")  # 设定数据文件所在的目录为工作目录,如果脚本与数据文件在同一目录,则不需要设定
pathdata <- read.table("ex3.11.dat")                                            # 读取数据文件到pathdata,pathdata是自己命名,可以随便定
names(pathdata) <-c(paste("y", 1:3, sep=""),paste("x", 1:3,sep=""))             # 给变量命名
pathmodel <- ' y1 ~ x1 +x2 +x3                                                  # 设置模型,pathmodel还是自己根据情况命名。因为是路径分析,用的~
               y2 ~ x1 +x2 +x3
               y3 ~ x2 +y1 +y2'
pathfit <- sem(pathmodel, pathdata)                                              # 进行SEM,pathfit为自己的名称,方便后面调用
summary(pathfit,fit.measures="TRUE")                                             # 显示SEM的总体结果
fitMeasures(pathfit,fit.measures="all", baseline.model=NULL)                     # 显示所有拟合指数
standardizedSolution(pathfit)                                                    # 显示标准化的结果

结果

> summary(pathfit,fit.measures="TRUE")                                             # 显示SEM的总体结果
lavaan (0.5-23.1097) converged normally after  44 iterations

  Number of observations                           500

  Estimator                                         ML
  Minimum Function Test Statistic                0.757
  Degrees of freedom                                 3
  P-value (Chi-square)                           0.860

Model test baseline model:

  Minimum Function Test Statistic             4107.449
  Degrees of freedom                                12
  P-value                                        0.000

User model versus baseline model:

  Comparative Fit Index (CFI)                    1.000
  Tucker-Lewis Index (TLI)                       1.002

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)              -4556.552
  Loglikelihood unrestricted model (H1)      -4556.174

  Number of free parameters                         12
  Akaike (AIC)                                9137.105
  Bayesian (BIC)                              9187.680
  Sample-size adjusted Bayesian (BIC)         9149.591

Root Mean Square Error of Approximation:

  RMSEA                                          0.000
  90 Percent Confidence Interval          0.000  0.040
  P-value RMSEA <= 0.05                          0.972

Standardized Root Mean Square Residual:

  SRMR                                           0.001

Parameter Estimates:

  Information                                 Expected
  Standard Errors                             Standard

Regressions:
                   Estimate  Std.Err  z-value  P(>|z|)
  y1 ~                                                
    x1                0.992    0.043   22.979    0.000
    x2                2.001    0.045   44.618    0.000
    x3                3.052    0.045   68.274    0.000
  y2 ~                                                
    x1                2.935    0.050   59.002    0.000
    x2                1.992    0.052   38.556    0.000
    x3                1.023    0.051   19.869    0.000
  y3 ~                                                
    x2                1.046    0.072   14.539    0.000
    y1                0.507    0.020   25.494    0.000
    y2                0.746    0.020   37.918    0.000

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)
   .y1                1.061    0.067   15.811    0.000
   .y2                1.408    0.089   15.811    0.000
   .y3                1.717    0.109   15.811    0.000

> fitMeasures(pathfit,fit.measures="all", baseline.model=NULL)                     # 显示所有拟合指数
               npar                fmin               chisq                  df              pvalue 
             12.000               0.001               0.757               3.000               0.860 
     baseline.chisq         baseline.df     baseline.pvalue                 cfi                 tli 
           4107.449              12.000               0.000               1.000               1.002 
               nnfi                 rfi                 nfi                pnfi                 ifi 
              1.002               0.999               1.000               0.250               1.001 
                rni                logl   unrestricted.logl                 aic                 bic 
              1.001           -4556.552           -4556.174            9137.105            9187.680 
             ntotal                bic2               rmsea      rmsea.ci.lower      rmsea.ci.upper 
            500.000            9149.591               0.000               0.000               0.040 
       rmsea.pvalue                 rmr          rmr_nomean                srmr        srmr_bentler 
              0.972               0.005               0.005               0.001               0.001 
srmr_bentler_nomean         srmr_bollen  srmr_bollen_nomean          srmr_mplus   srmr_mplus_nomean 
              0.001               0.001               0.001               0.001               0.001 
              cn_05               cn_01                 gfi                agfi                pgfi 
           5164.205            7496.574               1.000               1.000               0.143 
                mfi                ecvi 
              1.002               0.050 
> standardizedSolution(pathfit)                                                    # 显示标准化的结果
   lhs op rhs est.std    se       z pvalue
1   y1  ~  x1   0.254 0.011  23.780      0
2   y1  ~  x2   0.495 0.010  51.513      0
3   y1  ~  x3   0.758 0.007 105.969      0
4   y2  ~  x1   0.759 0.008  92.913      0
5   y2  ~  x2   0.497 0.011  44.664      0
6   y2  ~  x3   0.256 0.012  20.578      0
7   y3  ~  x2   0.192 0.013  14.616      0
8   y3  ~  y1   0.375 0.015  25.561      0
9   y3  ~  y2   0.547 0.014  39.362      0
10  y1 ~~  y1   0.061 0.004  15.841      0
11  y2 ~~  y2   0.082 0.005  15.865      0
12  y3 ~~  y3   0.054 0.004  15.369      0
13  x1 ~~  x1   1.000 0.000      NA     NA
14  x1 ~~  x2   0.035 0.000      NA     NA
15  x1 ~~  x3  -0.052 0.000      NA     NA
16  x2 ~~  x2   1.000 0.000      NA     NA
17  x2 ~~  x3   0.089 0.000      NA     NA
18  x3 ~~  x3   1.000 0.000      NA     NA