View source: R/ndr.R View source: R/nda.R
ndr | R Documentation |
The main function of Generalized Network-based Dimensionality Reduction and Analysis (GNDA).
ndr(r,covar=FALSE,cor_method=1,cor_type=1,min_R=0,min_comm=2,Gamma=1,null_model_type=4,
mod_mode=6,min_evalue=0,min_communality=0,com_communalities=0,use_rotation=FALSE,
rotation="oblimin",weight=NULL)
r |
A numeric data frame |
covar |
If this value is FALSE (default), it finds the correlation matrix from the raw data. If this value is TRUE, it uses the matrix r as a correlation/similarity matrix. |
cor_method |
Correlation method (optional). '1' Pearson's correlation (default), '2' Spearman's correlation, '3' Kendall's correlation, '4' Distance correlation |
cor_type |
Correlation type (optional). '1' Bivariate correlation (default), '2' partial correlation, '3' semi-partial correlation |
min_R |
Minimal square correlation between indicators (default: 0). |
min_comm |
Minimal number of indicators per community (default: 2). |
Gamma |
Gamma parameter in multiresolution null modell (default: 1). |
null_model_type |
'1' Differential Newmann-Grivan's null model, '2' The null model is the mean of square correlations between indicators, '3' The null model is the specified minimal square correlation, '4' Newmann-Grivan's modell (default) |
mod_mode |
Community-based modularity calculation mode: '1' Louvain modularity, '2' Fast-greedy modularity, '3' Leading Eigen modularity, '4' Infomap modularity, '5' Walktrap modularity, '6' Leiden modularity (default) |
min_evalue |
Minimal eigenvector centrality value (default: 0) |
min_communality |
Minimal communality value of indicators (default: 0) |
com_communalities |
Minimal common communalities (default: 0) |
use_rotation |
FALSE no rotation (default), TRUE the rotation is used. |
rotation |
"none", "varimax", "quartimax", "promax", "oblimin", "simplimax", and "cluster" are possible rotations/transformations of the solution. "oblimin" is the default, if use_rotation is TRUE. |
weight |
The weights of columns. The defalt is NULL (no weights). |
NDA both works on low and high simple size datasets. If min_evalue=min_communality=com_communalities=0 than there is no feature selection.
communality |
Communality estimates for each item. These are merely the sum of squared factor loadings for that item. It can be interpreted in correlation matrices. |
loadings |
A standard loading matrix of class “loadings". |
uniqueness |
Uniqueness values of indicators. |
factors |
Number of found factors. |
scores |
Estimates of the factor scores are reported (if covar=FALSE). |
n.obs |
Number of observations specified or found. |
fn |
Factor name: NDA |
Call |
Callback function |
Zsolt T. Kosztyan*, Marcell T. Kurbucz, Attila I. Katona
e-mail*: kosztyan.zsolt@gtk.uni-pannon.hu
Kosztyan, Z. T., Kurbucz, M. T., & Katona, A. I. (2022). Network-based dimensionality reduction of high-dimensional, low-sample-size datasets. Knowledge-Based Systems, 109180. doi:10.1016/j.knosys.2022.109180
plot
, biplot
, summary
.
# Dimension reduction
data(swiss)
df<-swiss
p<-ndr(df)
summary(p)
plot(p)
biplot(p)
# Data reduction
# Distance is Euclidean's distance
# covar=TRUE means only the distance matrix is considered.
q<-ndr(1-normalize(as.matrix(dist(df))),covar=TRUE)
summary(q)
plot(q)
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