CFA | review & summary for CFA | Cryptocurrency library

In my code, I'm trying to display and hide an image via a link when a button is clicked. Right now, when I click the button, the image opens in a different tab in Chrome. But I want to display it in my app's page. I need to assign the link to the TypeScript to make some changes later but I couldn't figure it out. How can I do that?

HTML:

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

I have list1 let's say:

I'm following Eli Bendersky's blog on parsing the DWARF debug information. He shows an example of parsing the binary with DWARF version 2 in his blog. The frame base of a function (further used for retrieving local variables) can be retrieved from the location list:

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 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 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.

Let's suppose we have a confirmatory factor analysis CFA with three latent variables (visual, textual, and speed) and that we want to add a regression where speed is the outcome and visual and textual are the explanatory variables (that is to say, expand the CFA to a Structural Equation Model SEM).

Also, let's assume that we have the grouping variable school (2 categories). I want to know if the effects (regression paths) of textual and visual are the same for the two groups or if it is better to have different coefficients for every group.

How can I achieve this?

My main idea is to follow an analysis of invariance calculating a Weak model, a Scalar model, a Strict model, and finally a model where regression coefficients aer also constrained to be equal across groups. Then, if the rmsea and CFI for the last model show not major changes I can say that the coefficients can be assumed to be the same; on the contrary, if rmsea and CFI shows that quality of the model gets worse, it is better to use different estimates for every group.

An alternative would be to make sure the CFA has strict invariance and then add the regression. I could compare the non gruped SEM with the grouped SEM and make a decistion based on the quality of the two models.

I've used the cv::merge() function at the end of the following code, but it throws an unhandled exception when the compiler reaches to the cv::merge() function call.

I've tried both cv::Mat[] array and vector of cv::Mat as inputs, but it is still throws the C++ exception.

The purpose of the code is to extract the red channel of an underwater image, and apply some new values in order to enhance color distribution according to equation 8 of this reference (Color Correction Based on CFA and Enhancement Based on Retinex With Dense Pixels for Underwater Images).

It only works with cv::merge(planes, 1, image2); which returns one page of planes in image2. It must merge three planes in planes into image2 to give a color image not a gray.