ndrlm: Genearlized Network-based Dimensionality Reduction and...

In kzst/nda: Generalized Network-Based Dimensionality Reduction and Analysis

Genearlized Network-based Dimensionality Reduction and Regression (GNDR)

Description

The main function of Generalized Network-based Dimensionality Reduction and Regression (GNDR) for supervised learning.

Usage

ndrlm(Y,X,latents="in",dircon=FALSE,optimize=TRUE,cor_method=1,
                cor_type=1,min_comm=2,Gamma=1,
                null_model_type=4,mod_mode=1,use_rotation=FALSE,
                rotation="oblimin",pareto=FALSE,fit_weights=NULL,
                lower.bounds.x = c(rep(-100,ncol(X))),
                upper.bounds.x = c(rep(100,ncol(X))),
                lower.bounds.latentx = c(0,0,0,0),
                upper.bounds.latentx = c(0.6,0.6,0.6,0.3),
                lower.bounds.y = c(rep(-100,ncol(Y))),
                upper.bounds.y = c(rep(100,ncol(Y))),
                lower.bounds.latenty = c(0,0,0,0),
                upper.bounds.latenty = c(0.6,0.6,0.6,0.3),
                popsize = 20, generations = 30, cprob = 0.7, cdist = 5,
                mprob = 0.2, mdist=10, seed=NULL)

Arguments

Y	A numeric data frame of output variables
X	A numeric data frame of input variables
latents	The employs of latent variables: "in" employs latent-independent variables (default); "out" employs latent-dependent variables; "both" employs both latent-dependent and latent independent variables; "none" do not employs latent variable (= multiple regression)
dircon	Wether enable or disable direct connection between input and output variables (default=FALSE)
optimize	Optimization of fittings (default=TRUE)
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_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 (default), '2' Fast-greedy modularity, '3' Leading Eigen modularity, '4' Infomap modularity, '5' Walktrap modularity, '6' Leiden modularity
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.
pareto	in the case of multiple objectives TRUE (default value) provides pareto-optimal solution, while FALSE provides weighted mean of objective functions (see out_weights)
fit_weights	weights of fitting the output variables (weights of means of objectives)
lower.bounds.x	Lower bounds of weights of independent variables in GNDA
upper.bounds.x	Upper bounds of weights of independent variables in GNDA
lower.bounds.latentx	Lower bounds of hyper-parementers of GNDA for independent variables (values must be positive)
upper.bounds.latentx	Upper bounds of hyper-parementers of GNDA for independent variables (value must be lower than one)
lower.bounds.y	Lower bounds of weights of dependent variables in GNDA
upper.bounds.y	Upper bounds of weights of dependent variables in GNDA
lower.bounds.latenty	Lower bounds of hyper-parementers of GNDA for dependent variables (values must be positive)
upper.bounds.latenty	Upper bounds of hyper-parementers of GNDA for dependent variables (value must be lower than one)
popsize	size of population of NSGA-II for fitting betas (default=20)
generations	number of generations to breed of NSGA-II for fitting betas (default=30)
cprob	crossover probability of NSGA-II for fitting betas (default=0.7)
cdist	crossover distribution index of NSGA-II for fitting betas (default=5)
mprob	mutation probability of NSGA-II for fitting betas (default=0.2)
mdist	mutation distribution index of NSGA-II for fitting betas (default=10)
seed	default seed value (default=NULL, no seed)

Details

NDRLM is a variable fitting with feature selection based on the tunes of GNDA method with NSGA-II algorithm for parameter fittings.

Value

Call	Callback function
fval	Objective function for fitting
hyperparams	optimized hyperparameters
pareto	in the case of multiple objectives TRUE provides pareto-optimal solution, while FALSE (default) provides weighted mean of objective functions (see out_weights)
Y	A numeric data frame of output variables
X	A numeric data frame of input variables
latents	Latent model: "in", "out", "both", "none"
NDAin	GNDA object, which is the result of model reduction and features selection in the case of employing latent-independent variables
NDAin_weight	Weights of input variables (used in ndr)
NDAin_min_evalue	Optimized minimal eigenvector centrality value (used in ndr)
NDAin_min_communality	Optimized minimal communality value of indicators (used in ndr)
NDAin_com_communalities	Optimized minimal common communalities (used in ndr)
NDAin_min_R	Optimized minimal square correlation between indicators (used in ndr)
NDAout	GNDA object, which is the result of model reduction and features selection in the case of employing latent-dependent variables
NDAout_weight	Weights of input variables (used in ndr)
NDAout_min_evalue	Optimized minimal eigenvector centrality value (used in ndr)
NDAout_min_communality	Optimized minimal communality value of indicators (used in ndr)
NDAout_com_communalities	Optimized minimal common communalities (used in ndr)
NDAout_min_R	Optimized minimal square correlation between indicators (used in ndr)
fits	List of linear regrassion models
otimized	Wheter fittings are optimized or not
NSGA	Outpot structure of NSGA-II optimization (list), if the optimization value is true (see in mco::nsga2)
extra_vars.X	Logic variable. If direct connection (dircon=TRUE) is allowed not only the latent but the excluded input variables are analyized in the linear models as extra input variables.
extra_vars.Y	Logic variable. If direct connection (dircon=TRUE) is allowed not only the latent but the excluded output variables are analyized in the linear models as extra input variables.
dircon_X	The list of input variables which are directly connected to output variables.
dircon_Y	The list of output variables which are directly connected to output variables.
fn	Function (regression) name: NDLM

Author(s)

Zsolt T. Kosztyan*, Marcell T. Kurbucz, Attila I. Katona

e-mail*: [e-mail]

References

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

See Also

ndr, plot, summary, mco::nsga2.

Examples

# Using NDRLM with curve fitting optimization

X<-freeny.x
Y<-freeny.y
NDRLM<-ndrlm(Y,X)
summary(NDRLM)

# Using NDRLM without fitting optimization

NDRLM<-ndrlm(Y,X,optimize=FALSE)
summary(NDRLM)

kzst/nda documentation built on Dec. 16, 2024, 7:02 a.m.