I am actually trying to perform a Generalized SCHUR decomposition in R. It looks like the package MATRIX only do the usuall SCHUR decomposition. I would like to know if there is any other package that does it. I read in some blog that we can actually call lapack function(DGGES.f) in R. I don't know how to do since DGGES depends on another external subroutine (SELCTG) to be defined by the user.
Your help will much appreciate.
Thanks in advance
Related
The article below mentions Shapley-Lorenz as an alternate computation to regular Shapley values for XAI. For implementing Shapley-Lorenz, so far the only code I found was something started in Python here --> https://github.com/roye10/ShapleyLorenz. Has anyone found code in R that calculates Shapley-Lorenz? I know R has a Shapley function but not sure how to adapt it to Shapley-Lorenz.
Alternatively, if Shapley-Lorenz is a reach for R and not practical to implement, has anyone tried mean Shapley values in R? I assume that might be an easier path but am not sure how to adapt it.
Both above are twists I am considering for a class assignment requirement, so any guidance is deeply appreciated. Thanks!
Giudici, P., & Raffinetti, E. (2021). Shapley-Lorenz eXplainable artificial intelligence. Expert Systems with Applications, 167, 114104.
I'm searching for a package or a way to use the algorithm Adaboost.R2 (adaboost for regression) in R. I can use it in ROOT and change my loss function in "quadratic" and I want to get the same thing in R. Someone can help me? Thanks.
I am working on clustering of variables in matlab. Two functions come in ClustOfVar package in R, called hcluster() and cutreevar().
I am good in Matlab and would like to use alternatives of hcluster() and cutreevar() in it.
Does Matlab has any inbuilt function which computes exactly same as hcluster() and cutreevar() does in R?
Need help.
Thanks
for heirarchical clustering you'll probably want to look at clusterdata. Note that you'll need the statistical toolbox for this function.
I have used biglm in R and found it very useful. Now I need the same type of functionality in python. Any ideas? I have seen that patsy/statsmodels has an incremental mode, but have not been able to find any samples to copy/adapt. Any pointers would be much appreciated.
from a related answer of Nathaniel Smith on the statsmodels mailing list
My incremental LS code might be useful here, it's basically the same
problem:
https://github.com/njsmith/pyrerp/blob/master/pyrerp/incremental_ls.py#L330
The new X'X is the sum of the old X'Xs, then you have to re-do the
scaling and inversion to get the new vcov matrix for the estimates.
Should be doable so long as you know how many data points are in each
and the various sums-of-squares. (The code I linked has some extra
complexity because of handling a particular sort of heteroskedasticity
via FGLS, but it can pretty much be ignored.)
statsmodels doesn't have anything in this area yet.
There is an incremental OLS function in statsmodels, however that was written as helper function for cusum tests (in memory) and hasn't been used or checked for any other purpose:
http://statsmodels.sourceforge.net/devel/generated/statsmodels.stats.diagnostic.recursive_olsresiduals.html
I have large data in my database. I have to create a matrix has size 600.000x20.000 or like that, but many of cells will be empty. How can I use this R programming language to create my matrix or by using singular value decomposition(SVD) methods? I do not know using in language R and I'll use the sparse matrix in Java programming? I am so confused...
It seems that your answer is here. You may check SparseM or spam packages as well.