Dear Posit Community,
unfortunately I have a problem with my lavaan output in R Studio (Version R-4.3.0). I would be very grateful to get help with my problem! I do not get standard errors, z and p values displayed. For understanding here is my multiple mediation model and the corresponding output:
mbiMediation <- '
mbi_100 ~ b1*scs_100 + b2*erq_reap_100 + b3*erq_supp_100 + b4*sci_adapt_100 + b5*sci_maladapt_100 + c1*nfc_100
scs_100 ~ a1*nfc_100
erq_reap_100 ~ a2*nfc_100
erq_supp_100 ~ a3*nfc_100
sci_adapt_100 ~ a4*nfc_100
sci_maladapt_100 ~ a5*nfc_100
#indirect effects
indirect1 := a1*b1
indirect2 := a2*b2
indirect3 := a3*b3
indirect4 := a4*b4
indirect5 := a5*b5
#contrasts
contrast1 := indirect1 - indirect2
contrast2 := indirect1 - indirect3
contrast3 := indirect1 - indirect4
contrast4 := indirect1 - indirect5
contrast5 := indirect2 - indirect3
contrast6 := indirect2 - indirect4
contrast7 := indirect2 - indirect5
contrast8 := indirect3 - indirect4
contrast9 := indirect3 - indirect5
contrast10 := indirect4 - indirect5
#total effect
total1 := c1 + (a1*b1) + (a2*b2) + (a3*b3) + (a4*b4) + (a5*b5)
scs_100 ~~ erq_reap_100
scs_100 ~~ erq_supp_100
scs_100 ~~ sci_adapt_100
scs_100 ~~ sci_maladapt_100
erq_reap_100 ~~ erq_supp_100
erq_reap_100 ~~ sci_adapt_100
erq_reap_100 ~~ sci_maladapt_100
erq_supp_100 ~~ sci_adapt_100
erq_supp_100 ~~ sci_maladapt_100
sci_adapt_100 ~~ sci_maladapt_100
'
> fit_mbiMediation <- sem(mbiMediation,
+ data = score_ZK,
+ se = "bootstrap",
+ bootstrap = 2000)
Warning message:
In lav_model_nvcov_bootstrap(lavmodel = lavmodel, lavsamplestats = lavsamplestats, :
lavaan WARNING: 94 bootstrap runs failed or did not converge.
> summary(fit_mbiMediation,
+ fit.measures = TRUE,
+ standardize = TRUE,
+ rsquare = TRUE,
+ estimates = TRUE,
+ ci = TRUE)
lavaan 0.6.15 ended normally after 180 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 27
Number of observations 641
Model Test User Model:
Test statistic 0.000
Degrees of freedom 0
Model Test Baseline Model:
Test statistic 780.091
Degrees of freedom 21
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.000
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -15894.983
Loglikelihood unrestricted model (H1) -15894.983
Akaike (AIC) 31843.966
Bayesian (BIC) 31964.468
Sample-size adjusted Bayesian (SABIC) 31878.745
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.000
P-value H_0: RMSEA <= 0.050 NA
P-value H_0: RMSEA >= 0.080 NA
Standardized Root Mean Square Residual:
SRMR 0.000
Parameter Estimates:
Standard errors Bootstrap
Number of requested bootstrap draws 2000
Number of successful bootstrap draws 1906
Regressions:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper Std.lv Std.all
mbi_100 ~
scs_100 (b1) -0.037 0.044 -0.839 0.401 -0.124 0.050 -0.037 -0.037
erq_r_100 (b2) -0.110 0.046 -2.409 0.016 -0.200 -0.017 -0.110 -0.093
erq_s_100 (b3) 0.132 0.036 3.677 0.000 0.061 0.202 0.132 0.135
sc_dp_100 (b4) -0.382 0.041 -9.284 0.000 -0.465 -0.301 -0.382 -0.364
sc_ml_100 (b5) 0.117 0.035 3.390 0.001 0.053 0.190 0.117 0.120
nfc_100 (c1) -0.088 0.037 -2.359 0.018 -0.161 -0.015 -0.088 -0.104
scs_100 ~
nfc_100 (a1) 0.509 0.029 17.377 0.000 0.451 0.566 0.509 0.600
erq_reap_100 ~
nfc_100 (a2) 0.067 0.029 2.294 0.022 0.010 0.126 0.067 0.093
erq_supp_100 ~
nfc_100 (a3) 0.008 0.035 0.226 0.822 -0.058 0.076 0.008 0.009
sci_adapt_100 ~
nfc_100 (a4) 0.158 0.032 4.927 0.000 0.094 0.220 0.158 0.194
sci_maladapt_100 ~
nfc_100 (a5) -0.286 0.033 -8.787 0.000 -0.349 -0.221 -0.286 -0.330
Covariances:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper Std.lv Std.all
.scs_100 ~~
.erq_reap_100 7.910 8.309 0.952 0.341 -8.119 25.056 7.910 0.040
.erq_supp_100 -9.700 10.756 -0.902 0.367 -31.708 12.436 -9.700 -0.040
.sci_adapt_100 4.412 8.980 0.491 0.623 -12.515 22.374 4.412 0.020
.sci_maldpt_100 -37.666 10.492 -3.590 0.000 -58.000 -17.619 -37.666 -0.166
.erq_reap_100 ~~
.erq_supp_100 54.565 11.224 4.861 0.000 34.294 77.074 54.565 0.214
.sci_adapt_100 89.884 11.498 7.817 0.000 67.045 112.267 89.884 0.385
.sci_maldpt_100 -4.120 9.076 -0.454 0.650 -21.689 13.918 -4.120 -0.017
.erq_supp_100 ~~
.sci_adapt_100 -30.795 11.249 -2.737 0.006 -53.338 -9.432 -30.795 -0.109
.sci_maldpt_100 24.683 11.044 2.235 0.025 3.460 46.593 24.683 0.085
.sci_adapt_100 ~~
.sci_maldpt_100 -17.616 9.514 -1.852 0.064 -36.845 0.939 -17.616 -0.066
Variances:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper Std.lv Std.all
.mbi_100 216.307 12.310 17.572 0.000 190.214 239.234 216.307 0.732
.scs_100 187.961 10.840 17.340 0.000 166.842 208.953 187.961 0.639
.erq_reap_100 211.379 12.955 16.316 0.000 184.933 236.328 211.379 0.991
.erq_supp_100 308.096 15.672 19.660 0.000 278.117 337.580 308.096 1.000
.sci_adapt_100 258.464 15.656 16.509 0.000 228.760 288.977 258.464 0.962
.sci_maldpt_100 273.102 17.671 15.455 0.000 239.342 307.939 273.102 0.891
R-Square:
Estimate
mbi_100 0.268
scs_100 0.361
erq_reap_100 0.009
erq_supp_100 0.000
sci_adapt_100 0.038
sci_maldpt_100 0.109
Defined Parameters:
Estimate Std.Err z-value P(>|z|) ci.lower ci.upper Std.lv Std.all
indirect1 -0.019 NA -0.064 0.025 -0.019 -0.022
indirect2 -0.007 NA -0.018 -0.000 -0.007 -0.009
indirect3 0.001 NA -0.009 0.010 0.001 0.001
indirect4 -0.060 NA -0.087 -0.036 -0.060 -0.071
indirect5 -0.034 NA -0.057 -0.014 -0.034 -0.039
contrast1 -0.012 NA -0.058 0.035 -0.012 -0.014
contrast2 -0.020 NA -0.064 0.026 -0.020 -0.023
contrast3 0.041 NA -0.009 0.094 0.041 0.049
contrast4 0.015 NA -0.036 0.068 0.015 0.017
contrast5 -0.008 NA -0.023 0.004 -0.008 -0.010
contrast6 0.053 NA 0.027 0.080 0.053 0.062
contrast7 0.026 NA 0.005 0.051 0.026 0.031
contrast8 0.061 NA 0.035 0.089 0.061 0.072
contrast9 0.035 NA 0.012 0.059 0.035 0.041
contrast10 -0.027 NA -0.058 0.008 -0.027 -0.031
total1 -0.207 NA -0.268 -0.141 -0.207 -0.244
I was running another models which additionally included control variables. Strangely enough, I got all Standard Errors, z-values, and p-values here. I already tried bootstrapping of 5000 and 10000. Further I tried this code:
parameterEstimates(fit_mbiMediation, ci = TRUE)
I also checked my datasets and the model syntax. Maybe I missed some mistakes. Anyway, I would be very grateful if anyone of you has some ideas for that issue.
Kindest regards!