fsdam
FS-DAM NLDR
Forward stepwise deep autoencoder-based monotone nonlinear dimension reduction.
Usage
fsdam(dat, opt_numCode = ncol(dat), opt_seed = 1, opt_model = "n", opt_gpu = 0,
opt_k = 100, opt_nEpochs = 10000,
opt_constr = c("newpenalization", "constrained", "none"),
opt_tuneParam = 10, opt_penfun = "mean", opt_ortho = 1, opt_earlystop = "no",
verbose = FALSE)# S3 method for fsdam
plot (x, which=c("mse", "history", "decoder.func", "scatterplot"),
k=NULL, dim.1=NULL, dim.2=NULL, col.predict=2, ...)
Arguments
- dat
data frame.
- opt_numCode
number of components to extract
- opt_seed
seed for torch
- opt_model
n for newpenalization
- opt_gpu
zero-based index of gpu to be used among all gpus. If negative, then no gpu is used
- opt_k
number of nodes in the coding/decoding layers
- opt_nEpochs
number of epochs for training
- opt_constr
constraint string
- opt_tuneParam
tuning parameter for monotonicity penalty
- opt_penfun
penalize sum or mean
- opt_ortho
tuning parameter for orthogonality penalty
- opt_earlystop
whether to stop early
- verbose
- x
fsdam object
- which
- k
the component to plot
- dim.1
index of the first variable
- dim.2
index of the second variable
- col.predict
color of the predicted curve when which = scatterplot
- …
plotting arguments
Details
If the torch python package is not available, this function will stop.
To make sure the right python installation is used, run reticulate::use_python("/app/easybuild/software/Python/3.7.4-foss-2016b/bin/python") in R before running this function for the first time.
References
Fong, Y, Xu, J. Multi-Stage Simultaneous Deep Autoencoder-based Monotone (MSS-DAM) Nonlinear Dimensionality Reduction Methods, Journal of Computational and Graphical Statistics, in press.
Examples
# NOT RUN {
# }
# NOT RUN {
fit=fsdam(hvtn505tier1[1:100,-1], opt_numCode=2, verbose=TRUE)
fit
plot(fit,which="mse")
plot(fit,which="history")
# }
# NOT RUN {
# }