CFA with Nas

[Andrea Araujo]

Hi all. I am new with SEM and lavaan. I am running the next model

myModel <- '

   # latent variables

     Connectivity=~Longitud_vias+L_orden_1+L_orden_2+

              L_orden_3+L_orden_4+L_orden_5+L_orden_6+Total+

              Num_Riesgo+Promedio_DistanciaCA2018+Promedio_DistanciaLB2018

     LULCC=~PorcentajeDef16_18+PorcentajeBosque+PorcentajeAgropecuario+

            PorcentajeCA+PorcentajeVA+PorcentajeZA+PorcentajeZI

     

   # regressions

     Casos2018 ~ Connectivity+LULCC

     

   # residual covariances

     Connectivity~~LULCC'

fit1<-cfa(myModel, data=alldata)

And I got this error:

Error in lav_samplestats_from_data(lavdata = lavdata, lavoptions = lavoptions,  : 

  lavaan ERROR: data contains no observations

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_data_full(data = data, group = group, cluster = cluster,  :

  lavaan WARNING: some observed variances are larger than 1000000

  lavaan NOTE: use varTable(fit) to investigate

3: In lav_data_full(data = data, group = group, cluster = cluster,  :

  lavaan WARNING: small number of observations (nobs < nvar)

  nobs = 0 nvar = 19

I guess it is because some of my observations contain no data or NAs, thus I am wondering how to deal with Nas? 

Thank you

[Terrence Jorgensen]

Error in lav_samplestats_from_data(lavdata = lavdata, lavoptions = lavoptions,  : 

  lavaan ERROR: data contains no observations

Is this the case after listwise deletion (the default)?  Maybe try FIML

https://lavaan.ugent.be/tutorial/est.html

Terrence D. Jorgensen

Assistant Professor, Methods and Statistics

Research Institute for Child Development and Education, the University of Amsterdam

http://www.uva.nl/profile/t.d.jorgensen

[Andrea Araujo]

Dear Terrence,

Thank you very much! It worked in the sense that I have no errors. Nevertheless I keep on having the same warnings. Could you guide a bit about what these warnings mean?

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_data_full(data = data, group = group, cluster = cluster,  :

  lavaan WARNING: some observed variances are larger than 1000000

  lavaan NOTE: use varTable(fit) to investigate

3: In lav_data_full(data = data, group = group, cluster = cluster,  :

  lavaan WARNING: due to missing values, some pairwise combinations have less than 10% coverage

4: In lav_mvnorm_missing_h1_estimate_moments(Y = X[[g]], wt = WT[[g]],  :

  lavaan WARNING:

    Maximum number of iterations reached when computing the sample

    moments using EM; use the em.h1.iter.max= argument to increase the

    number of iterations

5: In lavaan::lavaan(model = myModel, data = alldata, missing = "ml.x",  :

  lavaan WARNING:

    the optimizer warns that a solution has NOT been found!

Thank you!

[Pat Malone]

Try dividing the variable with the massive variance by 100. Greatly disparate variances can cause computational difficulties. No testing will be affected, just the scaling of paths (and SEs) involving that variable.

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Patrick S. Malone, PhD

Sr Research Statistician, FAR HARBΦR

+1 803.553.4181  |  pat@  |  farharbor.com

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