lavaan | an R package for structural equation modeling and more | Machine Learning library

I am trying to do a CFA for the first time. Lavaan gives the following error.

What is the reason for CFI=0 in a sem model in Lavaan. Statistic values are attached

I want to draw the following diagram in R:

I saw that the lavaan package has a function to build diagrams, however you need to provide an already fitted model. I just want to build the diagram above with my own coefficients and use it in Rmarkdown. Is there any straightforward way to achieve this? Colors are not important.

I am currently working on running a SEM analysis in Lavaan and I am running into a few problems. Before running the full sem, I intended to run a CFA to replicate the psychometric testing done with this measure I am using. This measure has 24 items, which make up 5 subscales (latent variables), which in turn load onto a total "higher-order" factor. I try to estimate this model in two different ways: (1) A five-factor model (without a higher order factor) in which all 5 subscales are allowed to correlate and (2) a higher-order model with a TOTAL latent variable made up of those 5 suscales.

The first model has five correlated latents (FNR, FOB...FAA) factors, with variance fixed to 1. This model converges without errors and fits the data. The second model also works, as long as I don't specify that the subscales (FNR, FOB..) that make up the FTOTAL latent variable are correlated. However, if I specified that these subscales are correlated (#Residual correlations part), the model still runs but gives me the error "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." If I remove the residuals correlation from Model 2, the model runs without error. The R code for both is the following:

I am trying to analyze three repeated measures of two outcome variables. It was recommended to use a latent growth curve model. I know in some software (SPSS) you can make growth curves with multiple measures, but it doesn't seem as straightforward in lavaan. Reading the lavaan tutorial it mentions multilevel SEM using sem() - is this appropriate for a repeated measures dataset? Or is there another package that allows multiple outcome growth curves in R?

I am trying to use semPlot::semPaths to make a plot of a bifactor model I ran in lavaan. The documentation for semPlot and qgraph is really great, so I've been able to figure out most of the specifications I want, but there's one thing I'm not sure is fixable. As you can see in the plot below, when a latent factor (e.g., "ext") is connected to a wide range of manifest variables, it becomes difficult to track which arrows/labels are associated with which manifest variables. (For example, it's hard to tell that the ext loading for adhd3 is .23.) It looks like the arrow is trying to connect to the center of the manifest variable labels, rather than the closest side. Is there any way to change this so it's a bit easier to read? Thank you so much!

Here is the code I used (sorry it's clunky-- 1) I'm new to this and 2) the latent variables were automatically in weird places so I defined their positions manually):

I am trying to plot a path diagram of a Structural Equation Model(SEM) in r. I was able to plot it using semPlot::semPaths(). The output is similar to The SEM was modeled using lavaan package.

I want a plot similar to . with estimates and p values. Can anyone help me out. Thanks in advance.

I am a rather new user of lavaan and have been trying to build a moderator model with a continuous moderator and an interaction term with a latent variable. I would like to hear your feedback on my code and especially whether my approach seems appropriate regarding adding the interaction term afterwards (as it requires saving the latent variable in the data frame). Just to give a short description of my study: I investigate the relationship between stress and burnout, and whether social support moderates this association. Unfortunately, I don’t have the actual data yet, so I cannot give information on the possible warning/error messages.

I am currently working on running a structural equation modelling analysis with a dataset and I am running into a few problems. Before running the full sem, I intended to run a CFA to replicate the psychometric testing done with this measure I am using. This measure has 24 items, which make up 5 subscales (latent variables), which in turn load onto an "total" higher order factor. In the literature they describe that "In all models, the items were constrained to load on one factor only, error terms were not allowed to correlate, and the variance of the factors was fixed to 1".

I've constraint items to load onto one factor, and set the variance of those factors to 1, but I am having trouble specifying in my model that the error terms are not allowed to correlate. Do they mean the error term of the items are not allowed to correlate? Is there an easy way to do this in lavaan or do I have to literally go "y1~~ 0y2","y1~~0y3".. and so on for every item?

Thank you in advance for the help.