quality,dimRedResultmethod {dimRed}  R Documentation 
Quality Criteria for dimensionality reduction.
Description
A collection of functions to compute quality measures on
dimRedResult
objects.
Usage
## S4 method for signature 'dimRedResult'
quality(.data, .method = dimRedQualityList(), .mute = character(0), ...)
dimRedQualityList(filter = FALSE)
Arguments
.data 
object of class 
.method 
character vector naming one of the methods 
.mute 
what output from the embedding method should be muted. 
... 
the pameters, internally passed as a list to the
quality method as 
filter 
filter methods by installed packages 
Value
a number
Methods (by class)

dimRedResult
: Calculate a quality index from a dimRedResult object.
Implemented methods
Method must be one of "Q_local", "Q_global",
"mean_R_NX", "total_correlation",
"cophenetic_correlation", "distance_correlation",
"reconstruction_rmse"
Rank based criteria
Q_local
, Q_global
, and mean_R_NX
are
quality criteria based on the Coranking matrix. Q_local
and Q_global
determine the local/global quality of the
embedding, while mean_R_NX
determines the quality of the
overall embedding. They are parameter free and return a single
number. The object must include the original data. The number
returns is in the range [0, 1], higher values mean a better
local/global embedding.
Correlation based criteria
total_correlation
calculates the sum of the mean squared
correlations of the original axes with the axes in reduced
dimensions, because some methods do not care about correlations
with axes, there is an option to rotate data in reduced space to
maximize this criterium. The number may be greater than one if more
dimensions are summed up.
cophenetic_correlation
calculate the correlation between the
lower triangles of distance matrices, the correlation and distance
methods may be specified. The result is in range [1, 1].
distance_correlation
measures the independes of samples by
calculating the correlation of distances. For details see
dcor
.
Reconstruction error
reconstruction_rmse
calculates the root mean squared error
of the reconstrucion. object
requires an inverse function.
Author(s)
Guido Kraemer
References
Lueks, W., Mokbel, B., Biehl, M., Hammer, B., 2011. How to Evaluate Dimensionality Reduction?  Improving the Coranking Matrix. arXiv:1110.3917 [cs].
Szekely, G.J., Rizzo, M.L., Bakirov, N.K., 2007. Measuring and testing dependence by correlation of distances. Ann. Statist. 35, 27692794. doi:10.1214/009053607000000505
Lee, J.A., PeluffoOrdonez, D.H., Verleysen, M., 2015. Multiscale similarities in stochastic neighbour embedding: Reducing dimensionality while preserving both local and global structure. Neurocomputing, 169, 246261. doi:10.1016/j.neucom.2014.12.095
See Also
Other Quality scores for dimensionality reduction:
AUC_lnK_R_NX,dimRedResultmethod
,
LCMC,dimRedResultmethod
,
Q_NX,dimRedResultmethod
,
Q_global,dimRedResultmethod
,
Q_local,dimRedResultmethod
,
R_NX,dimRedResultmethod
,
cophenetic_correlation,dimRedResultmethod
,
distance_correlation,dimRedResultmethod
,
mean_R_NX,dimRedResultmethod
,
plot_R_NX()
,
reconstruction_error,dimRedResultmethod
,
reconstruction_rmse,dimRedResultmethod
,
total_correlation,dimRedResultmethod
Examples
## Not run:
embed_methods < dimRedMethodList()
quality_methods < dimRedQualityList()
scurve < loadDataSet("Iris")
quality_results < matrix(NA, length(embed_methods), length(quality_methods),
dimnames = list(embed_methods, quality_methods))
embedded_data < list()
for (e in embed_methods) {
message("embedding: ", e)
embedded_data[[e]] < embed(scurve, e, .mute = c("message", "output"))
for (q in quality_methods) {
message(" quality: ", q)
quality_results[e, q] < tryCatch(
quality(embedded_data[[e]], q),
error = function (e) NA
)
}
}
print(quality_results)
## End(Not run)