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Load the package in the workspace.
Load other useful packages in the workspace.
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)
Set the model family to Poisson since the response matrix contain count data.
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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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