fsdam: FS-DAM NLDR

In FSDAM: Forward Stepwise Deep Autoencoder-Based Monotone NLDR

Description

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 class '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: 
    
fit=fsdam(hvtn505tier1[1:100,-1], opt_numCode=2, verbose=TRUE)
fit
plot(fit,which="mse")
plot(fit,which="history")


## End(Not run)

FSDAM documentation built on Nov. 20, 2020, 5:10 p.m.