Convergence | secure alternative to the Certificate Authority system

You have nodes that are only appearing in the Target column, so you need to incorporate that column when finding all unique nodes. I did this by concatenating the two columns (along with Label), grouping by node ID while summing the Label values, and then replacing summed labels with 1 if the sum was > 0:

The dimensions of y0 and g seems to be wrong. Reduce them by one dimension:

For other readers here, it seems like this is the implementation being asked about in this question.

The method you are using to try to predict labels works with labels for nodes, not edges. To visualize this, I plotted your example data and colored the plot by your Weight and Label columns (code to produce plot appended below) where Weight is the line thickness of the edge and Label is the color:

In order to use this method, you will need to produce data that looks like this, where each node (denoted by ID) gets exactly one node_label:

You need to fetch the data from the year variable twice, year @ 100 .... I think after that ?adaptday will work. There is forth word within \ n lo hi -- flag ; flag is True if lo <= n < hi for checking numbers within ranges,

In Forth it's unusual to use so many variables. The values are normally stored on the stack. j as a variable could override the j used as the outer do loop counter. I've seen k used for the next outer loop too!!

I'd implement it something like this. I can then run the words in the console with stack input to see what is happening to help debug.

The only reasonable solution which we came up was since the cost was high.. it was not possible to use this approach for this solution. We tried using a different approach like simple linear regression and it worked.

In my experience the dry principle is more important when writing libraries and code that you want to reuse. However my experience is that if you want to make a report then sometimes making things to dry can actually make things harder to maintain. I.e. sometimes you want to be able to change an individual graph/plot/piece of data in one place without it affecting the rest of the report. It takes a bit of practice and experience to find out what works best for you. In this particular use case be less focussed on following the DRY rule then when writing a library or an application.

Additionally if I had to make such a report and the situation would allow it. I would make in in a Jupyter notebook. Here you can nicely mix code with text and graphical output.

You either need to disable the dependencyConvergence check in the POM, or you need to add an entry to the section of your POM which contains the version of error_prone_annotations that you want to use.

There are a number of issues here.

The proximal problem is that you have a (near) singular fit: glmmTMB is trying to make the variance zero (5.138e-08 is as close as it can get). Because it fits on a log-variance (actually log-standard-deviation) scale, this means that it's trying to go to -∞, which makes the covariance matrix of the parameters impossible to estimate.

The main reason this is happening is that you have a very small number of groups (3) in your random effect (experiment).

These are extremely common issues with mixed models: you can start by reading ?lme4::isSingular and the relevant section of the GLMM FAQ.

The simplest solution would be to treat experiment as a fixed effect, in which case you would no longer have a mixed model and you could back to plain glm().

Another slightly worrying aspect of your code is the response variable cbind(SARA_ph58, 1). If SARA_ph58 is a binary (0/1) variable you can use just SARA_ph58. If you pass a two-column matrix as you are doing, the first column is interpreted as the number of successes and the second column as the number of failures; it looks like you may have been trying to specify that the total number of trials for each observation is 1 (again, if this is the case you can just use SARA_ph58 as the response).

One final note is that lme4::glmer is a little more tolerant of singular fits than glmmTMB.

The results do not make sense because you are sampling without considering the ordering of the sample matrix. Try the following