test statistics unavailable (tli, cfi, rmsea) and Error in TEST[[2]] : subscript out of bounds

[marieelyse.l...@gmail.com]

Hi ran the following script:

subsample_one_item_present2 <- NEW[which((rowMeans(is.na(NEW[c("a","b")]))) <= 1/2),]

dim(subsample_one_item_present2)

data2 <- subsample_one_item_present2[c("a","b")]

sink('model_teacher.txt')

model2 <- '

A =~ a + b'

fit2 <- cfa(model2, data = data2, estimator = "MLR", missing = "FIML", std.ov = TRUE, std.lv = TRUE, orthogonal = T, verbose = T)

sink()

o <- lavInspect(fit2, what ='fit')

capture.output(o, file = "fit_indices_model_fiml_onefactor_teacher.txt")

s <- summary(fit2, standardized = T, fit.measures = T)

capture.output(s, file = "model_output_model_fiml_onefactor_teacher.txt")

sink()

The model ran fine wihout any error messages, however when I run the lavInspect or summary, I get:

Error in TEST[[2]] : subscript out of bounds

what can I do to fix this and get the cfi, tli, rmsea?

Thank you very much

[Edward Rigdon]

A factor model with only 2 indicators is not statistically identified. I am surprised that lavaan did not turn up that diagnosis but instead produced a "subscript out of bounds" error. Perhaps, per your call to the cfa() function, the function both standardized the common factor AND fixed the first loading to 1, thus reducing the number of estimated parameters to 3, the same as the number of unique elements in the covariance matrix of the two observed variables. You could make the model identified by imposing an equality constraint on the two loadings.

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[marieelyse.l...@gmail.com]

Hi Edward,

Thank you for your suggestion. I just constrained the model like so 

model2 <- '

c==0.5

A =~ c*a + c*a'

This was my output :

lavaan 0.6-7 ended normally after 10 iterations

  Estimator                                         ML

  Optimization method                           NLMINB

  Number of free parameters                          6

  Number of equality constraints                     2

                                                      

  Number of observations                           151

  Number of missing patterns                         3

                                                      

Model Test User Model:

                                               Standard      Robust

  Test Statistic                                 99.330          NA

  Degrees of freedom                                  1           1

  P-value (Chi-square)                            0.000          NA

  Scaling correction factor                                      NA

       Yuan-Bentler correction (Mplus variant)                     

Model Test Baseline Model:

  Test statistic                               159.268     116.971

  Degrees of freedom                                 1           1

  P-value                                        0.000       0.000

  Scaling correction factor                                  1.362

User Model versus Baseline Model:

  Comparative Fit Index (CFI)                    0.379          NA

  Tucker-Lewis Index (TLI)                       0.379          NA

                                                                  

  Robust Comparative Fit Index (CFI)                            NA

  Robust Tucker-Lewis Index (TLI)                               NA

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)               -306.734    -306.734

  Scaling correction factor                                  0.935

      for the MLR correction                                      

  Loglikelihood unrestricted model (H1)       -257.069    -257.069

  Scaling correction factor                                  1.116

      for the MLR correction                                      

                                                                  

  Akaike (AIC)                                 621.469     621.469

  Bayesian (BIC)                               633.538     633.538

  Sample-size adjusted Bayesian (BIC)          620.878     620.878

Root Mean Square Error of Approximation:

  RMSEA                                          0.807       0.000

  90 Percent confidence interval - lower         0.677       0.000

  90 Percent confidence interval - upper         0.945       0.000

  P-value RMSEA <= 0.05                          0.000         NaN

                                                                  

  Robust RMSEA                                                  NA

  90 Percent confidence interval - lower                     0.000

  90 Percent confidence interval - upper                     0.000

Standardized Root Mean Square Residual:

  SRMR                                           0.385       0.385

Parameter Estimates:

  Standard errors                             Sandwich

  Information bread                           Observed

  Observed information based on                Hessian

and I had the following error message for lavInspect:

Warning message:

In pchisq(XX2, df = df2, ncp = ncp) : NaNs produced

What would be the reason for not obtaining the robust fit measures?

Thank you very much again

[Edward Rigdon]

     I would remove the line where you constrain c to be 0.5. Given that both observed variables and factor are standardized, that 0.5 has a specific meaning, and may not be appropriate for your data.

     It might be that the NaN's happen because the fit of the model is so poor, but that is just a guess.

--Ed R.

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[marieelyse.l...@gmail.com]

ok, thanks I thought that is how I would apply the equality constrain that you suggested.

thanks