Fits nmfae with a paired decoder/encoder rank
(\(Q = R\)) across a range of ranks and reports r.squared,
the effective rank (of the latent encoding \(H\)), and the
element-wise CV error sigma.ecv, with the same concise plot as
nmfkc.rank. For a full \((Q, R)\) grid use
nmfae.ecv with rank.encoder and its heatmap.
Usage
nmfae.rank(
Y1,
Y2 = Y1,
rank = 1:5,
detail = c("full", "fast"),
plot = TRUE,
...
)Arguments
- Y1
Endogenous matrix (\(P_1 \times N\)).
- Y2
Exogenous matrix; defaults to
Y1(autoencoder).- rank
Integer vector of (paired) ranks to evaluate.
- detail
"full"(default) also runs element-wise CV (sigma.ecv);"fast"skips it (plots r.squared and eff.rank only, and recommends the R-squared elbow).- plot
Logical; draw the diagnostics plot (default
TRUE).- ...
Passed on to
nmfaeandnmfae.ecv(e.g.\maxit,nfolds,seed).
Value
A list with rank.best and criteria
(rank, effective.rank, effective.rank.ratio,
r.squared, sigma.ecv).
References
Roy, O., & Vetterli, M. (2007). The effective rank: A measure of
effective dimensionality. Proc. EUSIPCO, 606–610.
(effective.rank)
Wold, S. (1978). Cross-validatory estimation of the number of
components in factor and principal components models.
Technometrics, 20(4), 397–405. (sigma.ecv)