sem error

[Vasiliki]

Hello,

I am new to lavaan and I am having some trouble with my code. I was wondering if I can get some help in this group :)

This is my code:

data1 <- read.csv("data1.csv", header =T)

model1 <- 'cog =~ cog1 + cog2 + cog3; soc =~ soc_1 + soc_2 + soc_3; reg =~ reg1 + reg2 + reg3 + reg4 + reg5 + reg6 + reg7; out =~ out1 + out2 + out3; cog ~ out; soc ~ out; reg ~ out'

SEMmodel <- cfa(model=model1, data=data1,missing = "fiml", estimator = "mlr", se = "robust.huber.white", debug = TRUE)

And this is the error I get:

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: small number of observations (nobs < nvar)

3: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats,  :

  lavaan WARNING:

    Could not compute standard errors! The information matrix could

    not be inverted. This may be a symptom that the model is not

    identified.

4: In lav_test_yuan_bentler(lavobject = NULL, lavsamplestats = lavsamplestats,  :

  lavaan WARNING: could not invert information matrix

5: In lav_object_post_check(object) :

  lavaan WARNING: covariance matrix of latent variables

                is not positive definite;

                use lavInspect(fit, "cov.lv") to investigate.

6: In lavaan::lavaan(model = modelideation, data = mrimpact_apq_numeric,  :

  lavaan WARNING: not all elements of the gradient are (near) zero;

                  the optimizer may not have found a local solution;

                  use lavInspect(fit, "optim.gradient") to investigate

I thought that if I normalise the data and if I use some dimensionality reduction method it might help with the first two errors, but I am not sure. Also not sure how to address the other 4 warnings.

Any help would be very much appreciated.

With best wishes,

Vasiliki

[Jeremy Miles]

On Fri, 14 Jun 2019 at 07:38, 'Vasiliki' via lavaan <lav...@googlegroups.com> wrote:

Hello,

I am new to lavaan and I am having some trouble with my code. I was wondering if I can get some help in this group :)

This is my code:

data1 <- read.csv("data1.csv", header =T)

model1 <- 'cog =~ cog1 + cog2 + cog3; soc =~ soc_1 + soc_2 + soc_3; reg =~ reg1 + reg2 + reg3 + reg4 + reg5 + reg6 + reg7; out =~ out1 + out2 + out3; cog ~ out; soc ~ out; reg ~ out'

SEMmodel <- cfa(model=model1, data=data1,missing = "fiml", estimator = "mlr", se = "robust.huber.white", debug = TRUE)

And this is the error I get:

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

Look at your variances (use apply(data1, 2, var) ). Any variables that have really big variances, divide them by a large number to make the variances within about an order of magnitude of each other. 

 

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

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

Your sample is too small. What is your sample size?

 

3: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats,  :

  lavaan WARNING:

    Could not compute standard errors! The information matrix could

    not be inverted. This may be a symptom that the model is not

    identified.

Probably due to warning 2. 

 

4: In lav_test_yuan_bentler(lavobject = NULL, lavsamplestats = lavsamplestats,  :

  lavaan WARNING: could not invert information matrix

Probably due to warning 2. 

 

5: In lav_object_post_check(object) :

  lavaan WARNING: covariance matrix of latent variables

                is not positive definite;

                use lavInspect(fit, "cov.lv") to investigate.

Probably due to warning 2, but address the other issues first. 

 

6: In lavaan::lavaan(model = modelideation, data = mrimpact_apq_numeric,  :

  lavaan WARNING: not all elements of the gradient are (near) zero;

                  the optimizer may not have found a local solution;

                  use lavInspect(fit, "optim.gradient") to investigate

May be due to warning 2. 

 

I thought that if I normalise the data and if I use some dimensionality reduction method it might help with the first two errors, but I am not sure. Also not sure how to address the other 4 warnings.

Normailizing won't help. Standardizing might. (Depending on exactly what you mean by normalizing). 

Dimensionality reduction might, but that's not something you can just apply without knowing a lot about the data and the theory.

 

Any help would be very much appreciated.

You're welcome,

Jeremy