| Title: | Generalised Linear Models by Subsampling and One-Step Polishing |
| Version: | 1.0.0 |
| Description: | Fast fitting of generalised linear models on moderately large datasets, by taking an initial sample, fitting in memory, then evaluating the score function for the full data in the database. Thomas Lumley <doi:10.1080/10618600.2019.1610312>. |
| Imports: | DBI, tidypredict, rlang, methods, tidyverse, dbplyr, vctrs, knitr, dplyr, purrr, tibble, tidyr, stringr |
| Suggests: | RSQLite, duckdb, bigrquery, testthat (≥ 3.0.0) |
| License: | MIT + file LICENSE |
| Maintainer: | Shangqing Cao <caoalbert@g.ucla.edu> |
| RoxygenNote: | 7.1.1 |
| Encoding: | UTF-8 |
| Depends: | R (≥ 2.10) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2021-06-23 03:00:58 UTC; apple |
| Author: | Thomas Lumley [aut, cph], Shangqing Cao [ctb, cre] |
| Repository: | CRAN |
| Date/Publication: | 2021-06-23 08:00:02 UTC |
Fast generalized linear model in a database
Description
Fast generalized linear model in a database
Usage
dbglm(formula, family = binomial(), tbl, sd = FALSE,
weights = .NotYetImplemented(), subset = .NotYetImplemented(), ...)
Arguments
... |
This argument is required for S3 method extension. |
formula |
A model formula. It can have interactions but cannot have any transformations except |
family |
Model family |
tbl |
An object inheriting from |
sd |
Experimental: compute the standard deviation of the score as well as the mean in the update and use it to improve the information matrix estimate |
weights |
We don't support weights |
subset |
If you want to analyze a subset, use |
Details
For a dataset of size N the subsample is of size N^(5/9). Unless N is large the approximation won't be very good. Also, with small N it's quite likely that, eg, some factor levels will be missing in the subsample.
Value
A list with elements
tildebeta |
coefficients from subsample |
hatbeta |
final estimate |
tildeV |
variance matrix from subsample |
hatV |
final estimate |
References
http://notstatschat.tumblr.com/post/171570186286/faster-generalised-linear-models-in-largeish-data
Data of vehicles registered in New Zealand as of November 2017
Description
Data of vehicles registered in New Zealand as of November 2017
Usage
data(fleet1)
Format
A tibble with 10000 rows and 34 variables:
- basic_colour
chracter colour of the car
- power_rating
numeric horsepower of the car
- gross_vehicle_mass
numeric mass of the vehicle in kg
- number_of_seats
numeric number of seats in the car