covaraince between exogenous variables

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unordered

unread,
Nov 15, 2018, 5:14:18 AM11/15/18
to lavaan
Hey @all,

I have several questions:
First of all I want to specify the covariance between exogenous variables

q1: the lavaan-default does not estimate the covariance between exogenous variables?

If lavaan does not estimate the covariance, q2 it is possible to modify the default with:
fixed.x = FALSE
to estimate the covariance.

I have some exogenous categorical variables in my model and it works well. But if i fix my code with
sem(model, data=x, fixed.x = FALSE)

I get an error message:
lavaan ERROR: unordered factor(s) detected; make them numeric or ordered:


I know this error very well from my beginning with lavaan, but I fixed it soon (Tutorial). So the error message only comes, when I modify my code with fixed.x = False

I hope it is not a redundant problem and I am looking forward to your help

thanks,
marco

Christopher David Desjardins

unread,
Nov 15, 2018, 11:53:10 AM11/15/18
to lav...@googlegroups.com
On Thu, Nov 15, 2018 at 4:14 AM unordered <team.pro...@gmail.com> wrote:
Hey @all,

I have several questions:
First of all I want to specify the covariance between exogenous variables

q1: the lavaan-default does not estimate the covariance between exogenous variables?

That's correct.
 

If lavaan does not estimate the covariance, q2 it is possible to modify the default with:
fixed.x = FALSE
to estimate the covariance.

You would set fixed.x = TRUE

I have some exogenous categorical variables in my model and it works well. But if i fix my code with
sem(model, data=x, fixed.x = FALSE)

I get an error message:
lavaan ERROR: unordered factor(s) detected; make them numeric or ordered:


You need to fix this and as the message says either make theM factors (look at str(x)) and change them to numeric or ordered variables. This is unrelated to your fixed.x = FALSE questions though.

I know this error very well from my beginning with lavaan, but I fixed it soon (Tutorial). So the error message only comes, when I modify my code with fixed.x = False

I hope it is not a redundant problem and I am looking forward to your help

thanks,
marco

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unordered

unread,
Nov 16, 2018, 4:43:50 AM11/16/18
to lavaan
Hey Christopher,

it goes forward, but I still have some questions:

I fixed the problem with the categorical variables.
Now I want to be sure about estimation of exogenous variable

lets say i have the follow simple code:

mod <-
'lat_var1 =~ x1 + x2 + x3
lat_var2 =~ x4 + x5 + x6

#Regression
lat_var1 ~ var_1 + var_2
lat_var2 ~ var_1 + var_2

#now i want the covariance between the exogenous variables (var_1 and var_2)
var_1 ~~ var_2

#in the SEM-Function I set fixed.x= TRUE

mod.fit <- sem(mod, data=data, fixed.x=TRUE)

now i estimate freely the covariance between var_1 and var_2 and the results are different from the results without covariance.

There is no difference in the output between
fixed.x = TRUE

#and

fixed.x FALSE

What is the difference in the Argument TRUE and FALSE?

again: thanks a lot for your help!

marco

Christopher David Desjardins

unread,
Nov 16, 2018, 6:33:23 PM11/16/18
to lav...@googlegroups.com
It's because you are overriding the fixed.x = TRUE with the exogenous covariance. Presumably, you saw this WARNING message when you specified fixed.x = TRUE.

lavaan WARNING: syntax contains parameters involving exogenous covariates; switching to fixed.x = FALSE


It says that because you are specifying a covariance and it's telling you that specifying a covariance and setting that to TRUE makes no sense and therefore lavaan is overriding your decision. 


Compare:


model <- ' 

  # latent variable definitions

     ind60 =~ x1 + x2 + x3

     dem65 =~ y5 + y6 + y7


  # regressions

    dem65 ~ y1 + y2

    ind60 ~ y1 + y2


  # residual correlations

    y1 ~~ y2

'

fit <- sem(model, data=PoliticalDemocracy)

summary(fit)


to:


model <- ' 

  # latent variable definitions

     ind60 =~ x1 + x2 + x3

     dem65 =~ y5 + y6 + y7


  # regressions

    dem65 ~ y1 + y2

    ind60 ~ y1 + y2

'

fit <- sem(model, data=PoliticalDemocracy, fixed.x = FALSE)

summary(fit)


to:


model <- ' 

  # latent variable definitions

     ind60 =~ x1 + x2 + x3

     dem65 =~ y5 + y6 + y7


  # regressions

    dem65 ~ y1 + y2

    ind60 ~ y1 + y2

'

fit <- sem(model, data=PoliticalDemocracy, fixed.x = TRUE)

summary(fit)





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