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I’ve designed a 3D model in SketchUp and I didn’t use any texture. I’m faced with an issue related with lagging on mouse move and rotate process. When I exported the model by Dae format and imported to the three js online editor (three js online editor) mouse movement is being very slow. I think it occurs fps drop. I couldn’t understand what’s problem with my model that I designed. I need your suggestions and ideas how to resolve this issue. Thanks for your support. I’ve uploaded 3D model’s image. Please take a look.

Object Count: 98.349, Vertices: 2,107.656, Triangles: 702.552

To assess if there is an association between certain groups of patients (patient_group; categorical variable) and a disease (disease_outcome; count variable) I am running negative binomial regression models (due to overdispersion). To check for confounding by other variables I am running 3 models with increasing amounts of covariates.

To display the IRRs and CIs i want to use the tbl_regression function from the package gtsummary (I am using the latest version 1.3.7.9022). However, calling the function returns the IRR and the corresponding 95% CIs non-exponentiated, even though I put exponentiate=TRUE:

Due to privacy issues I don't have the original raw data matrices, but instead I can have covariance matrices of x and y (x'x, y'y, x'y) datasets or the correlation matrix between the two of them (or any other sort of matrix that is not the original data matrix).

I need to find a way to apply canonical correlation analysis directly on those matrices. Browsing the net I didn't find any solution to my problem. I want to ask if there is already an implemented algorithm able to work on these data, in R would be the best, but other languages are ok

Example from the tutorial in R for cca package:
(https://stats.idre.ucla.edu/r/dae/canonical-correlation-analysis/)

mm <- read.csv("https://stats.idre.ucla.edu/stat/data/mmreg.csv")
colnames(mm) <- c("Control", "Concept", "Motivation", "Read", "Write", "Math", "Science", "Sex")

You divide the dataset into x and y :
x <- mm[, 1:3]
y <- mm[, 4:8]

Then the function works taking as input these two datasets: cc(x,y) (note that the function standardizes the data by itself).

What I want to know if there is a way to perform cca starting by centering matrices around the mean: x = scale(x, scale = F) y = scale(Y, scale = F)

An then computing the covariance matrices x'x, y'y, xy'xy:
cvx = crossprod(x); cvy = crossprod(y); cvxy = crossprod(x,y)

And the algorithm should take in input those matrices to work and compute the canonical variates and correlation coefficients like: f(cvx, cvy, cvxy)

In this article is written a solution starting from covariance matrices for example, but I don't if it is just theory or someone has actually implemented it http://graphics.stanford.edu/courses/cs233-20-spring/ReferencedPapers/CCA_Weenik.pdf

I hope to be exhaustive enough!

I'm trying to implement skinning using skeletal animations stored in a Collada file, and while I managed to load it and render the model without skinning correctly, I can't figure out why when I apply my skinning algorithm all the parts get bunched up at the model's feet, or extremely deformed. The entire project is stored on GitHub for reference (the skinning branch).

I believe the vertex shader is correct since if I pass identity transforms to the bones I get my default pose model, it's calculating the bone transforms based on the skeletal animation in the .dae file that's somehow broken. This is what my problem looks like, versus how the model looks like in the default pose:

I believe my problem is somewhere while applying the recursive bone transforms:

I am pretty much brand new to all things python, and much to my chagrin I have been trying to produce a fairly straight forward OHLC chart. Code below with dataframe samples. I am trying to plot and save an OHLC chart, for a single stock, on a single trading day, in 1m ticks. The yaxis appears to working fine, however the chart when shown is blank. The xaxis is showing the starting time of 09:30 but with no other 1m ticks. Moving the chart over the blank figure shows values for the yaxis but the x= nada. Example

What I am hoping to eventually achieve, is the xaxis label to show the time, in minutes, no dae required, 90 degrees rotated, at say 15min intervals. I would rather an OHLC chart than a candlestick, but I also want it to be decipherable, as I have seen many versions that are just a blur of tiny vertical lines that are no use to anyone. If the size needs to be stretched horizontally in order to fit the some 376 1m records in the dataframe, then so be it. If it is too cluttered then I would like to be able to space out the tick interval perhaps to every 2 or 5 mins. The xaxis xticks should still remain at 15min intervals however. I would like to then save the result as a jpg.

I have tried so many variations of mplfinace, now no longer know what is the most recent of valid module. I have tried both 'quotes' and values in the candlestick_ohlc statement, there seems to be no apparent difference. I have read and re-read and tried so many examples but all seems to fail at the translation of the time in all things to do with the xaxis and it is very confusing for me to understand and beyond frustrating .. heh.

If anyone could kindly point me in the right direction here I would be very grateful for any and all assistance.

Many thanks, Tim.D

I have some code that gets a dataframe and stores the data in JSON. Then from there I take that JSON and move it to another dictionary - However, it is poorly formed for what I need it to be.

Right now the data looks like this:

I am trying to make it clear the difference between using inline integration and not using inline integration during simulation in Dymola.
Here is the basic procedure of the DASSL algorithm:

I find the following summary about the inline integration technique:

To summarise, inline integration harnesses the symbolic manipulation of Dymola to insert discretization expressions, representing the numerical integration algorithms, into the differential-algebraic equation (DAE) model to be solved. In effect, the inserted discretization expressions (representing the numerical integration algorithms) are solved “inline” with the model, rising to the name of the technique. The upshot is a DAE which is easier to solve numerically, thus leading to a shorter solve time.

1. as to the part of insert discretization expression into DAE model to be solved, I think DASSL does the same thing, as shown in the above screenshot, DASSL would insert the expression of y=h*der(y)+old(y) into the original DAE.
2. but what does it mean by inserted discretization expressions are solved inline with the model? In the DASSL algorithm, I just use Newton Method to solve the nonlinear equations of f(y-old(y)/h,y,t)=0, it seems natural. So what would happen if I use the inline integration method?

I often meet the initialization problem shown below, it says that some kind of non-linear solver will attempt to handle this problem. As I know it, Dymola uses Newton Method to solve the equation after getting the BLT form of the original DAE system, so what is this nonlinear solver exactly?

I am trying to understand the process of solving DAEs in Dymola. According to Chapter 13.8 of Dymola's help document, Dymola would first use an index reduction algorithm to transform the original DAE into ODE, then get a BLT form after partitioning and tearing, so Dymola could solve ODE with BDF method like DASSL.

Here is the screenshot of Dymola's help document.

I also found some material about how to solve DAE from Wolfram mathematica, as shown in the following screenshot, there could be different strategies in different phases, I marked the method I think adopted in Dymola. My question is:

1. Does Dymola just adopt the method as I marked?
2. Or Dymola also adopt the method as Wolfram Mathematica, which is using different methods in different phases? If so, how does Dymola determine using which method?