# SEM > model.sem <- ' + ###Measuremement models + + Immediate_causes =~ morb + month_bf + num_semfood + Underlying_causes =~ bmi + birth_weight + bord + Basic_causes =~ HH_members + w_index +m_educa + + ### Regression + + HAZ ~ Immediate_causes + Underlying_causes + Basic_causes + + + ###Residual correlation + + ' > fitsem <- sem(model.sem, data=data4R) Warning messages: 1: In lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some observed variances are (at least) a factor 1000 times larger than others; use varTable(fit) to investigate 2: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING: could not compute standard errors! lavaan NOTE: this may be a symptom that the model is not identified. 3: In lav_object_post_check(object) : lavaan WARNING: some estimated lv variances are negative > summary (fitsem) lavaan (0.6-1) converged normally after 874 iterations Used Total Number of observations 1540 3047 Estimator ML Model Fit Test Statistic 655.916 Degrees of freedom 30 P-value (Chi-square) 0.000 Parameter Estimates: Information Expected Information saturated (h1) model Structured Standard Errors Standard Latent Variables: Estimate Std.Err z-value P(>|z|) Immediate_causes =~ morb 1.000 month_bf 21.565 NA num_semfood 2.906 NA Underlying_causes =~ bmi 1.000 birth_weight 265.130 NA bord 2.112 NA Basic_causes =~ HH_members 1.000 w_index -4.917 NA m_educa 162.557 NA Regressions: Estimate Std.Err z-value P(>|z|) HAZ ~ Immediate_cass -134.195 NA Underlying_css 16.096 NA Basic_causes -99.388 NA Covariances: Estimate Std.Err z-value P(>|z|) Immediate_causes ~~ Underlying_css -0.004 NA Basic_causes -0.000 NA Underlying_causes ~~ Basic_causes -0.001 NA Variances: Estimate Std.Err z-value P(>|z|) .morb 2.533 NA .month_bf 7.182 NA .num_semfood 2.422 NA .bmi 10.660 NA .birth_weight 448275.026 NA .bord 2.036 NA .HH_members 4.444 NA .w_index 2.011 NA .m_educa 11.692 NA .HAZ 20577.163 NA Immediate_cass 0.071 NA Underlying_css 0.400 NA Basic_causes -0.000 NA