SEM tree

[Hyoshin Park]

Dear all, please help me and save my life!

when I ran the following model (lavaan): two latent variables (independent) to the one binary dependent variable (X9).

growthCurveModel <- '

    X9 ~ c*human + b*distraction;

    distraction ~ a*human;

    human =~ 1*X1 + 1*X6 + 1*X2;

    distraction =~ 1*X3 + 2*X8 + 3*X4;

    human ~~ vari*human; human ~ meani*1;

    distraction ~~ vars*distraction; distraction ~ means*1;

    X1 ~~ residual*X1; X1 ~ 0*1;

    X2 ~~ residual*X2; X2 ~ 0*1;

    X3 ~~ residual*X3; X3 ~ 0*1;

    X4 ~~ residual*X4; X4 ~ 0*1;

    X6 ~~ residual*X6; X6 ~ 0*1;

    X8 ~~ residual*X8; X8 ~ 0*1;

    X9 ~~ residual*X9; X9 ~ 0*1;

'

> run <- lavaan(growthCurveModel,dataset)

I have following warning messages:

Warning messages:

1: 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.

2: In lavaan(growthCurveModel, dataset) :

  lavaan WARNING: some estimated variances are negative

3: In lavaan(growthCurveModel, dataset) :

  lavaan WARNING: covariance matrix of latent variables is not positive definite; use inspect(fit,"cov.lv") to investigate.

When I tried to fix the dependent variable X9 to be ordered by using 

> run <- lavaan(growthCurveModel,dataset,ordered=c("X9"))

I have following errors: Error in B1 %*% Delta.g : non-conformable arguments

In addition: There were 50 or more warnings (use warnings() to see the first 50)

This is the dataset I used. (dataset <- data.frame(read.csv("C:\\Users\\hyoshin\\Desktop\\R SEMtree\\data2.txt",sep="\t", header=T)))

Thank you!!

X1	X6	X2	X3	X8	X4	X9
0	0	0	0	1	0	0
1	0	0	0	1	1	0
0	1	0	0	1	1	1
0	0	0	0	0	0	0
0	0	0	0	0	0	0
1	0	0	0	0	1	1
1	0	0	0	0	1	1
0	1	0	0	0	1	1
0	1	0	0	0	0	0
0	1	0	0	0	1	1
0	0	0	1	0	1	0
1	0	0	1	0	1	0
0	1	0	1	0	0	0
0	0	1	0	1	0	1
0	0	1	0	1	1	1
1	0	1	0	1	1	1
0	0	1	0	0	0	0
0	0	1	0	0	0	0
1	0	1	0	0	1	1
1	0	1	0	0	1	1
0	1	1	0	0	1	1
0	1	1	0	0	1	0
1	0	1	1	0	0	1
1	0	1	1	0	1	0
1	0	1	1	0	0	1
0	1	1	1	0	0	1

[Hyoshin Park]

I also have tried this 

model <- '

    X9 ~ human + distraction;

    distraction ~ human;

    human =~ X1 + X6 + X2;

    distraction =~ X3 + X8 + X4;

    X2 ~~ X2;

    X4 ~~ X4;

    X3 ~~ X1 + X6;

    X8 ~~ X8; 

    X9 ~~ X9; 

'

fit <- sem(model,dataset)

and I got this Warning message:

In lavaan::lavaan(model = model, data = dataset, model.type = "sem",  :

  lavaan WARNING: model has NOT converged!

[Hyoshin Park]

I have a third try with this! Thank you for helping me!!

model <- '

    X9 ~ c*human + b*distraction;

    distraction ~ a*human;

    human =~ 1*X1 + 1*X6 + 1*X2;

    distraction =~ 1*X3 + 2*X8 + 3*X4;

    human ~~ vari*human; human ~ meani*1;

    distraction ~~ vars*distraction; distraction ~ means*1;

    X1 ~~ e1 * X1;

    X2 ~~ e2 * X2;

    X3 ~~ e3 * X3;

    X4 ~~ e4 * X4;

    X6 ~~ e6 * X6;

    X8 ~~ e8 * X8;

    X9 ~~ e9 * X9;

'

fitMeasures(m1.fit<- lavaan(model,dataset, model.type='sem', sample.cov = x.cov, sample.nobs = N, control=list(x.tol=1e-6), estimator='ML', fixed.x = F, auto.var=F))

warning messages are 

               npar                fmin               chisq                  df              pvalue      baseline.chisq 

             14.000               0.572              29.736              21.000               0.097              36.734 

        baseline.df     baseline.pvalue                 cfi                 tli                nnfi                 rfi 

             21.000               0.018               0.445               0.445               0.445               0.191 

                nfi                pnfi                 ifi                 rni                logl   unrestricted.logl 

              0.191               0.191               0.445               0.445            -117.848            -102.980 

                aic                 bic              ntotal                bic2               rmsea      rmsea.ci.lower 

            263.697             281.310              26.000             237.855               0.126               0.000 

     rmsea.ci.upper        rmsea.pvalue                 rmr          rmr_nomean                srmr        srmr_bentler 

              0.224               0.142               0.049               0.037               0.169               0.169 

srmr_bentler_nomean         srmr_bollen  srmr_bollen_nomean          srmr_mplus   srmr_mplus_nomean               cn_05 

              0.168               0.165               0.157               0.171               0.166              29.566 

              cn_01                 gfi                agfi                pgfi                 mfi                ecvi 

             35.041               0.896               0.827               0.538               0.845                  NA 

Warning messages:

1: In lav_partable_check(lavpartable) :

  lavaan WARNING: missing intercepts are set to zero: [X9]

2: In lavaan(model, dataset, model.type = "sem", sample.cov = x.cov,  :

  lavaan WARNING: some estimated variances are negative

3: In lavaan(model, dataset, model.type = "sem", sample.cov = x.cov,  :

  lavaan WARNING: covariance matrix of latent variables is not positive definite; use inspect(fit,"cov.lv") to investigate.

> parameterEstimates(m1.fit, ci=T, boot.ci.type='norm')

           lhs op         rhs label    est    se      z pvalue ci.lower ci.upper

1           X9  ~       human     c -0.721 0.651 -1.106  0.269   -1.997    0.556

2           X9  ~ distraction     b  4.799 1.641  2.925  0.003    1.583    8.014

3  distraction  ~       human     a -0.068 0.117 -0.577  0.564   -0.298    0.162

4        human =~          X1        1.000 0.000     NA     NA    1.000    1.000

5        human =~          X6        1.000 0.000     NA     NA    1.000    1.000

6        human =~          X2        1.000 0.000     NA     NA    1.000    1.000

7  distraction =~          X3        1.000 0.000     NA     NA    1.000    1.000

8  distraction =~          X8        2.000 0.000     NA     NA    2.000    2.000

9  distraction =~          X4        3.000 0.000     NA     NA    3.000    3.000

10       human ~~       human  vari -0.049 0.023 -2.179  0.029   -0.094   -0.005

11       human ~1             meani  0.388 0.042  9.255  0.000    0.306    0.470

12 distraction ~~ distraction  vars  0.004 0.004  0.931  0.352   -0.004    0.011

13 distraction ~1             means  0.198 0.052  3.778  0.000    0.095    0.300

14          X1 ~~          X1    e1  0.275 0.069  3.994  0.000    0.140    0.410

15          X2 ~~          X2    e2  0.353 0.088  4.032  0.000    0.181    0.525

16          X3 ~~          X3    e3  0.213 0.060  3.567  0.000    0.096    0.331

17          X4 ~~          X4    e4  0.210 0.066  3.166  0.002    0.080    0.339

18          X6 ~~          X6    e6  0.247 0.062  3.971  0.000    0.125    0.368

19          X8 ~~          X8    e8  0.182 0.053  3.406  0.001    0.077    0.286

20          X9 ~~          X9    e9  0.214 0.093  2.289  0.022    0.031    0.397

21          X1 ~1                    0.000 0.000     NA     NA    0.000    0.000

22          X6 ~1                    0.000 0.000     NA     NA    0.000    0.000

23          X2 ~1                    0.000 0.000     NA     NA    0.000    0.000

24          X3 ~1                    0.000 0.000     NA     NA    0.000    0.000

25          X8 ~1                    0.000 0.000     NA     NA    0.000    0.000

26          X4 ~1                    0.000 0.000     NA     NA    0.000    0.000

27          X9 ~1                    0.000 0.000     NA     NA    0.000    0.000

[Seongho Bae]

Dear Hyoshin,

What are you want to do with latent growth modeling?; Why are you want to use latent growth modeling?

And your raw data has just 26 cases? That is response of test of the experimental psychology experiment?

Normally, ML is not fitting with binary data well. and structural equation modeling is not fitting with very small case well. Basically, structural equation modeling will be work with over 200 cases or parameter * 10 cases as known.

Please response my questions. I'll wait you.

—

Seongho Bae

Master of psychology (I/O Psychology)

2015년 2월 21일 토요일 오전 6시 48분 8초 UTC+9, Hyoshin Park 님의 말:

[yrosseel]

On 02/20/2015 10:48 PM, Hyoshin Park wrote:
> When I tried to fix the dependent variable X9 to be ordered by using
> > run <- lavaan(growthCurveModel,dataset,ordered=c("X9"))
>
> I have following errors: Error in B1 %*% Delta.g : non-conformable arguments

This is because you have provided parameters for the thresholds, and you
are using the lavaan() function (which in <= 0.5.17 does not add them
automatically).

Yves.