Initialization algorithms

Cristian Castiglione

Workspace setup

Load the package in the workspace.

library(sgdGMF)

Load other useful packages in the workspace.

library(ggplot2)
library(ggpubr)
library(reshape2)

Ant traits data

Load the ant traits data in the workspace and define the response matrix Y and covariate matrices X and Z.

# install.packages("mvabund")
# data(antTraits, package = "mvabund")

load(url("https://raw.githubusercontent.com/cran/mvabund/master/data/antTraits.RData"))

Y = as.matrix(antTraits$abund)
X = as.matrix(antTraits$env[,-3])
Z = matrix(1, nrow = ncol(Y), ncol = 1)

attr(Y, "dimnames") = NULL
attr(X, "dimnames") = NULL
attr(Z, "dimnames") = NULL

n = nrow(Y)
m = ncol(Y)

Model specification

Set the model family to Poisson since the response matrix contain count data.

family = poisson()
suppressWarnings({
  init_glm_dev = sgdGMF::sgdgmf.init(Y, X, Z, ncomp = 2, family = family, method = "glm", type = "deviance")
  init_glm_prs = sgdGMF::sgdgmf.init(Y, X, Z, ncomp = 2, family = family, method = "glm", type = "pearson")
  init_glm_lnk = sgdGMF::sgdgmf.init(Y, X, Z, ncomp = 2, family = family, method = "glm", type = "link")
  init_ols_dev = sgdGMF::sgdgmf.init(Y, X, Z, ncomp = 2, family = family, method = "ols", type = "deviance")
  init_ols_prs = sgdGMF::sgdgmf.init(Y, X, Z, ncomp = 2, family = family, method = "ols", type = "pearson")
  init_ols_lnk = sgdGMF::sgdgmf.init(Y, X, Z, ncomp = 2, family = family, method = "ols", type = "link")
})
data.frame(
  "Method" = rep(c("GLM", "OLS"), each = 3),
  "Resid" = rep(c("Deviance", "Pearson", "Link"), times = 2),
  "Deviance" = 
    list(init_glm_dev, init_glm_prs, init_glm_lnk, init_ols_dev, init_ols_prs, init_ols_lnk) |> 
    lapply(function (obj) round(100 * deviance(obj, normalize = TRUE), 2)) |> unlist() |> drop()
)
#>   Method    Resid Deviance
#> 1    GLM Deviance    37.16
#> 2    GLM  Pearson    27.65
#> 3    GLM     Link    45.47
#> 4    OLS Deviance    43.52
#> 5    OLS  Pearson    49.72
#> 6    OLS     Link    42.71