拟合度指数"NA“

Question

我在用Lavaan运行一个非递归模型。然而，有两件事情发生了，我不太明白。首先，拟合度指数和一些标准误差为"NA".第二，不同方向的两个变量之间的两个系数不一致(非递归部分:驻留迁移-作者)：一个是正的，另一个是负的(至少它们应该在同一个方向上；否则，如何解释？)有人能帮我吗？如果你想让我更多地澄清，请告诉我。谢谢!

model01<-'ResidentialMobility~a*Coun
SavingMotherPercentage~e*Affect
SavingMotherPercentage~f*Author
SavingMotherPercentage~g*Recipro

Affect~b*ResidentialMobility
Author~c*ResidentialMobility
Recipro~d*ResidentialMobility

ResidentialMobility~h*Affect
ResidentialMobility~i*Author
ResidentialMobility~j*Recipro

Affect~~Author+Recipro+ResidentialMobility
Author~~Recipro+ResidentialMobility
Recipro~~ResidentialMobility


Coun~SavingMotherPercentage

ab:=a*b
ac:=a*c
ad:=a*d

be:=b*e
cf:=c*f
dg:=d*g
'

fit <- cfa(model01, estimator = "MLR", data = data01, missing = "FIML")
summary(fit, standardized = TRUE, fit.measures = TRUE)

输出：

经过93次迭代，lavaan (0.5-21)正常收敛

                                                  Used       Total
  Number of observations                           502         506

  Number of missing patterns                         4

  Estimator                                         ML      Robust
  Minimum Function Test Statistic                   NA          NA
  Degrees of freedom                                -2          -2
  Minimum Function Value               0.0005232772506
  Scaling correction factor                           
    for the Yuan-Bentler correction

User model versus baseline model:

  Comparative Fit Index (CFI)                       NA          NA
  Tucker-Lewis Index (TLI)                          NA          NA

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)              -5057.346   -5057.346
  Loglikelihood unrestricted model (H1)      -5057.084   -5057.084

  Number of free parameters                         29          29
  Akaike (AIC)                               10172.693   10172.693
  Bayesian (BIC)                             10295.032   10295.032
  Sample-size adjusted Bayesian (BIC)        10202.984   10202.984

Root Mean Square Error of Approximation:

  RMSEA                                             NA          NA
  90 Percent Confidence Interval             NA     NA          NA     NA
  P-value RMSEA <= 0.05                             NA          NA

Standardized Root Mean Square Residual:

  SRMR                                           0.006       0.006

Parameter Estimates:

  Information                                 Observed
  Standard Errors                   Robust.huber.white

Regressions:
                           Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  ResidentialMobility ~                                                         
    Coun       (a)           -1.543    0.255   -6.052    0.000   -1.543   -0.540
  SavingMotherPercentage ~                                                      
    Affect     (e)            3.093    1.684    1.837    0.066    3.093    0.122
    Author     (f)            2.618    0.923    2.835    0.005    2.618    0.145
    Recipro    (g)            0.061    1.344    0.046    0.964    0.061    0.003
  Affect ~                                                                      
    RsdntlMblt (b)           -0.311    0.075   -4.125    0.000   -0.311   -0.570
  Author ~                                                                      
    RsdntlMblt (c)           -0.901    0.119   -7.567    0.000   -0.901   -1.180
  Recipro ~                                                                     
    RsdntlMblt (d)           -0.313    0.082   -3.841    0.000   -0.313   -0.512
  ResidentialMobility ~                                                         
    Affect     (h)           -0.209    0.193   -1.082    0.279   -0.209   -0.114
    Author     (i)            0.475    0.192    2.474    0.013    0.475    0.363
    Recipro    (j)            0.178    0.346    0.514    0.607    0.178    0.109
  Coun ~                                                                        
SvngMthrPr                0.003    0.001    2.225    0.026    0.003    0.108

Covariances:
                         Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
 .Affect ~~                                                                   
   .Author                  0.667    0.171    3.893    0.000    0.667    0.534
   .Recipro                 0.669    0.119    5.623    0.000    0.669    0.773
 .ResidentialMobility ~~                                                      
   .Affect                  0.624    0.144    4.347    0.000    0.624    0.474
 .Author ~~                                                                   
   .Recipro                 0.565    0.173    3.267    0.001    0.565    0.416
 .ResidentialMobility ~~                                                      
   .Author                  1.029    0.288    3.572    0.000    1.029    0.499
   .Recipro                 0.564    0.304    1.851    0.064    0.564    0.395

Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .ResidentlMblty    1.813       NA                      1.813    1.270
   .SvngMthrPrcntg   29.591    7.347    4.027    0.000   29.591    1.499
   .Affect            5.701    0.169   33.797    0.000    5.701    7.320
   .Author            5.569    0.275   20.259    0.000    5.569    5.109
   .Recipro           5.149    0.186   27.642    0.000    5.149    5.889
   .Coun              0.367    0.069    5.336    0.000    0.367    0.735

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .ResidentlMblty    2.169    0.259    8.378    0.000    2.169    1.064
   .SvngMthrPrcntg  363.792   23.428   15.528    0.000  363.792    0.934
   .Affect            0.797    0.129    6.153    0.000    0.797    1.314
   .Author            1.957    0.343    5.713    0.000    1.957    1.647
   .Recipro           0.941    0.126    7.439    0.000    0.941    1.231
   .Coun              0.242    0.004   54.431    0.000    0.242    0.969

Defined Parameters:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    ab                0.480    0.120    3.991    0.000    0.480    0.308
    ac                1.390    0.261    5.328    0.000    1.390    0.637
    ad                0.483    0.133    3.640    0.000    0.483    0.276
    be               -0.962    0.548   -1.757    0.079   -0.962   -0.070
    cf               -2.359    0.851   -2.771    0.006   -2.359   -0.171
    dg               -0.019    0.421   -0.046    0.964   -0.019   -0.001

Answer 1

我认为你之所以得到NA，是因为你指定了一个自由度为-2的模型。您应该以不同的方式指定模型，以便得到一个正数的自由度。