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Imputation of missing values with configurable neural networks, for
mixed-type data (impNNet()) and compositional data with
rounded zeros (impNNetCoDa()). The network architecture is
described by a deepimp_arch() object. Models run on a
native torch backend by default
(no Python required), or optionally on keras3.
# from the repository
remotes::install_bitbucket("matthias-da/deepimp")
# the default backend (native libtorch; no Python)
torch::install_torch()
# optional: the keras backend (Python + TensorFlow)
# install.packages("keras3"); keras3::install_keras()Mixed-type imputation:
library(deepImp)
data(sleep, package = "VIM")
imp <- impNNet(sleep, arch = deepimp_arch_small(), epochs = 50, seed = 1)
completed <- getImputed(imp)Compositional data (rounded zeros below a detection limit):
x <- data.frame(a = runif(50, 5, 10), b = runif(50, 5, 10), c = runif(50, 5, 10))
x$a[1:5] <- 0
imp <- impNNetCoDa(x, dl = c(1, 1, 1), label = 0, arch = deepimp_arch_small(), seed = 1)
getImputed(imp)See vignette("deepImp") for a full walkthrough.
Templ, M. (2021). Imputation of rounded zeros for compositional data using neural networks. In: Advances in Compositional Data Analysis (Festschrift). Springer.
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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