GGFL (v0.4.0)

This package gives GGFL estimates via coordinate descent algorithm.

cite this package of the latest version:
Ohishi, M. (2024). GGFL: Coordinate optimization for GGFL. R package version 0.4.0. https://github.com/ohishim/GGFL

information:

• (2024/08/14) attention ! There is a mistake in the v0.3.0 and v0.3.1. The mistake has been corrected in v0.4.0.

Installation

You can install the development version of GGFL like so:

devtools::install_github("ohishim/GGFL")

Example 1

This package has an example dataset as the two objects exdata and adj. The exdata has 500 individuals and 22 items of which labels are "y", "X1", ..., "X20", and "area". The "y" is a response variable, "X1", ..., "X20" are explanatory variables, and "area" is a variable of spatial information which has 20 areas.

This example requires the following package:

library(GGFL)
library(dplyr)
library(magrittr)

First, exdata is sorted by "area", like this:

exdata1 <- arrange(exdata, area)

Moreover, column names of adj are redefined as follows:

adj1 <- select(adj, area=areaNO, adj=adjNO)

Next, variables required for GGFL are extracted as follwos:

y <- exdata1$y
X <- select(exdata1, X1:X20) %>% data.matrix
area <- exdata1$area

Then, GGFL procedure can be executed as follows:

res <- GGFL(y, X, area, adj1)

In default, y and all column vectors of X are standardized in the sense of norm. If you do not want to do it, you can use the option standardize = FALSE. If you want to have the estimates and fitted values, you can respectively obtain as res$coefGGFL and res$fitGGFL. Here, these results are for the tuning parameter optimized by EGCV criterion. To obtain results for all candidates of tuning parameter, you can use the option out.all = TRUE.

Example 2

In the above example, all variables are penalized. When only partial variables are penalized, you can use pGGFL instead of GGFL, like this:

y <- exdata$y
X <- select(exdata, X1:X15) %>% cbind(X0=1, .)  #variables for penalized estimation
Z <- select(exdata, X16:X20)                    #variables for non-penalized estimation
area <- exdata$area

yli <- split(y, area)
Xli <- lapply(split(X, area), data.matrix)
Zli <- lapply(split(Z, area), data.matrix)
D <- split(adj$adjNO, adj$areaNO)

res <- pGGFL(yli, Xli, Zli, D)

Reference

1. Ohishi, M., Okamura, K., Itoh, Y., Wakaki, H. & Yanagihara, H. (2024). Coordinate descent algorithm for generalized group fused Lasso. Behaviormetrika (in press). doi: 10.1007/s41237-024-00233-6