dispersion | OpenRocket dispersion tool

I have a class that handles plotting new data via an inbuilt PubSub-style functionality. When a publisher sets a new value, it causes a call to update:

In this question / answer from 5 years ago about logLik.lm() and glm(), it was pointed out that code comments in the R stats module suggest that lm() and glm() are both internally calculating some kind of scale or dispersion parameter--presumably one which describes the estimated dispersion of the observation values being predicted by the regression.

This naturally begets another question: if it's truly a real parameter being estimated by the fit algorithm somewhere (or even if it's just some kind of implicit / effective parameter), how do I access this parameter from the resulting fit object?

I've produced a MWE (plus supporting setup / plot code) below:

• Part 1 constructs some simulated input data, which we'll fit to a straight line (implying two fit parameters are expected). Given the question is about a hidden, internally modeled dispersion parameter, I wanted to make sure the fit algorithm is forced to do something interesting, so therefore 10% of the points have been deliberately modeled as outliers. If you understand what's shown in the plot below, then you can probably skip reading this portion of the code.

• Part 2 is the main body of the MWE, illustrating the point that my question is asking about: it runs glm() on the input data and examines some of the results, demonstrating that logLik() claims three parameter estimates, in apparent disagreement with glm() which seems to give two.

• Part 3 just produces a little supplementary figure based on the input data and results. It's only included for completeness & reproducibility; you can probably skip reading it too.

Is it possible to get data from a kdeplot when its input is 2D? I have the following:

I'm performing 5-fold cross-validation using glm to perform logistic regression. Here is a reproducible example using the built-in cars dataset

Can someone help me to understand the amazing graphs generated by Weights & Biases tools when you are training a BERT model?

How can I interpret the above image? I don't know what the dispersion grey means, nor if the concentration in the blue region is good or bad.

Thanks in advance.

I have a column called 'cArticle' in excel, containing data in below format. I have a requirement to extract in another column the code that is in brackets. Ex- Z1A,F5C,etc

I have managed to get it partially working by using the below formula, but it still returns with values with the closing brackets. How can I modify it to get the desired results?

RIGHT(cArticle,4)

Thank you in advance!

I am trying to run a logistic regression model to predict the default probabilities of individual loans. I have a large sample size of 1.85 million observations, about 81% of which were fully paid off and the rest defaulted. I had run the logistic regression with 20+ other predictors that were statistically significant and got warning "fitted probabilities 0 or 1 occurred", and by adding predictors step by step I found that only 1 predictor was causing this problem, the "annual income" (annual_inc). I ran a logistic regression with only this predictor and found that it predicts only 0's (fully paid off loans), although there is a significant proportion of defaulted loans. I tried different proportions of training and testing data. If I give split the model in the way that gives 80% of the original sample to the Testing set and 20% to the Training set, R doesn't show the fitted probabilities warning, but the model still predicts 0's only on the testing set. Below I attach the little code concerned just in case. I doubt that adding a small sample of my data would be of any use given the circumstance, but if I am mistaken, let me know please and I will add it.

Currently trying differential expression between two groups. This code was working 6 months ago, but now I get : deseq2 library

I'm trying to create variational autoencoder and that means I need custom loss function. The problem is that inside loss function I have 2 different losses - mse and divergence. And mse is Tensor and divergence is KerasTensor ( because of dispersion and mu, I get out from encoder ). And I get such errors:

TypeError: Cannot convert a symbolic Keras input/output to a numpy array. This error may indicate that you're trying to pass a symbolic value to a NumPy call, which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs to a TF API that does not register dispatching, preventing Keras from automatically converting the API call to a lambda layer in the Functional Model.

So here is my architecture: