GFLglm v0.1.0

This package gives generalized fused Lasso estimator for generalized linear models via coordinate descent algorithm. Furthermore, this package includes the R package GFLlogit.

cite this package of the latest version:
Ohishi, M. (2024). GFLglm: Generalized fused Lasso for grouped data in generalized linear models. R package version 0.1.0. https://github.com/ohishim/GFLglm

Installation

You can install the current version of GFLglm like so:

devtools::install_github("ohishim/GFLglm")

Example

This example requires the following packages:

library(GFLglm)
library(magrittr)

This package has the dataset crimetko used in Ohishi (2024) like this

head(crimetko)
#>   year area crime pop group
#> 1 2017    1   714   8     1
#> 2 2017    1    76   5     1
#> 3 2017    1    44   4     1
#> 4 2017    1   275  22     1
#> 5 2017    1    61   4     1
#> 6 2017    1    47 526     1

The crime is the number of crimes of which the original data was collected by the Metropolitan Police Department, available at TOKYO OPEN DATA (https://portal.data.metro.tokyo.lg.jp/). This variable is arranged and used the following production:

• Tokyo Metropolitan Government & Metropolitan Police Department. The number of recognized cases by region, crime type, and method (yearly total; in Japanese), https://creativecommons.org/licenses/by/4.0/deed.en.

The pop is population of which the original data was obtained from the results of the population census, as provided in e-Stat (https://www.e-stat.go.jp/en). This variable is arranged and used.

This package also has the dataset adj of adjacent relationship of groups in crimetko like this

head(adj)
#>   g adj
#> 1 1   2
#> 2 1   3
#> 3 1   4
#> 4 1   5
#> 5 1   6
#> 6 1  54

For example, group 1 is adjacent to groups 2, 3, 4, 5, 6, 54, … For such a data, Poisson regression with generalized fused Lasso can be executed as

y <- crimetko$crime                           # the response variable  
group <- crimetko$group                       # group id
D <- adj %$% split(adj, g)                    # adjacent relationship
q <- crimetko$pop %>% inset(.==0, 1) %>% log  # offset for log-link

res <- GFLglm(y, group, D, "Poisson", offset=q)

The fourth parameter specifies the error distribution which can take “Gaussian”, “Binomial”, “Poisson”, “Negative.Binomial” (or “NB”), “Gamma”, or “Inverse.Gaussian” (or “IG”). For “Gamma”, “NB”, and “IG”, there is a option using log-link by setting link = "log".

Reference

1. Ohishi, M. (2024). Generalized fused Lasso for grouped data in generalized linear models. Stat. Comput., 34, 124. doi: 10.1007/s11222-024-10433-5