fastglm | Fast glm fitting via RcppEigen | Dataset library

by jaredhuling C++ Version: v0.0.1 License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | fastglm Summary

fastglm is a C++ library typically used in Artificial Intelligence, Dataset, Numpy applications. fastglm has no vulnerabilities and it has low support. However fastglm has 47 bugs. You can download it from GitHub.

The ‘fastglm’ package is a re-write of glm() using RcppEigen designed to be computationally efficient and algorithmically stable.

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Quality

Security

License

Reuse

Support

fastglm has a low active ecosystem.

It has 38 star(s) with 11 fork(s). There are 4 watchers for this library.

It had no major release in the last 12 months.

There are 5 open issues and 7 have been closed. On average issues are closed in 356 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of fastglm is v0.0.1

Quality

fastglm has 47 bugs (0 blocker, 0 critical, 15 major, 32 minor) and 0 code smells.

Security

fastglm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

fastglm code analysis shows 0 unresolved vulnerabilities.

There are 15 security hotspots that need review.

License

fastglm does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

fastglm releases are available to install and integrate.

Installation instructions are not available. Examples and code snippets are available.

It has 2524 lines of code, 0 functions and 19 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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fastglm Key Features

No Key Features are available at this moment for fastglm.

fastglm Examples and Code Snippets

Stability

C++

Lines of Code : 64

License : No License

Copy

set.seed(1)
x <- matrix(rnorm(10000 * 100), ncol = 100)
y <- (exp(0.25 * x[,1] - 0.25 * x[,3] + 0.5 * x[,4] - 0.5 * x[,5] + rnorm(10000)) ) + 0.1


system.time(gfit1 <- fastglm(cbind(1, x), y, family = Gamma(link = "sqrt")))

##    user  sys

Quick Usage Overview

C++

Lines of Code : 47

License : No License

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library(fastglm)

library(speedglm)
library(microbenchmark)
library(ggplot2)

set.seed(123)
n.obs  <- 10000
n.vars <- 100
x <- matrix(rnorm(n.obs * n.vars, sd = 3), n.obs, n.vars)
Sigma <- 0.99 ^ abs(outer(1:n.vars, 1:n.vars, FUN = "-"))

Installing the ‘fastglm’ package

C++

Lines of Code : 1

License : No License

Copy

devtools::install_github("jaredhuling/fastglm")

Community Discussions

Trending Discussions on fastglm

Fit many glm models: improve speed

QUESTION

Fit many glm models: improve speed

Asked 2019-Nov-03 at 14:35

I am writing a function to fit many glm models. To just give you some ideas about the function, I include a small section of my code. With the help of several SO users, the function works for my analysis purpose now. However, sometimes, particularly when the sample size is relatively small, it can take quite long time to finish the whole process. To reduce the time, I am considering changing some details of iterative maximization, such as maximum number of iterations. I have not found a way to do it, maybe because I am still not familiar with R terminology. Any suggestions to do this or other ways to reduce time would be appreciated.

...

ANSWER

Answered 2019-Nov-03 at 14:35

The IRLS algorithm typically used for fitting glms requires matrix inversion/decomposition at each iteration. fastglm offers several different options for the decomposition and the default choice is a slower but more stable option (QR with column-pivoting). If your only interest is speed, then one of the two available Cholesky-type decompositions will improve the speed dramatically, which would be more advisable than just changing the number of IRLS iterations. Another notable difference between fastglm and standard IRLS implementations is its careful use of half-steps in order to prevent divergence (IRLS can diverge in practice in a number of cases).

The method argument of fastglm allows one to change the decomposition. option 2 gives the vanilla Cholesky decomposition and option 3 gives a slightly more stable version of this. On my computer, the timings for your provided example are:

Source https://stackoverflow.com/questions/58442026

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install fastglm

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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