Publications by Jose Tamez
ILAA Tutorial
Figure 1: The UPLTM: any linear transformation matrix that does not reduce data dimensionality and has a one-to-one association between units of measurement, i.e., it is preserving the space metric. The ERT is special form of UPLTM that is preserving the metric, and aims to create latent variables with controlled association between them. 1 I...
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ILAA
1 Introduction Iterative Linear Association Analysis (ILAA) is a computational method that creates a linear transformation from a sample of multidimensional data that effectively removes linear associations between data variables. The returned transformation matrix can be used to: Do an exploratory analysis of latent variables and their assoc...
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70SignatureEvaluation
NIK and RRPlots The libraries library(survival) library(FRESA.CAD) ## Loading required package: Rcpp ## Loading required package: stringr ## Loading required package: miscTools ## Loading required package: Hmisc ## ## Attaching package: 'Hmisc' ## The following objects are masked from 'package:base': ## ## format.pval, units ## Loading r...
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RRPlots and Survival Biomarkers
1 Showcasing RRPlots 1.0.1 Libraries library(survival) library(FRESA.CAD) ## Loading required package: Rcpp ## Loading required package: stringr ## Loading required package: miscTools ## Loading required package: Hmisc ## ## Attaching package: 'Hmisc' ## The following objects are masked from 'package:base': ## ## format.pval, units ## Lo...
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RRPlots for Survival Model Evaluations
Evaluation of RISK survival models This document highlights the use of RRPlot(), CoxRiskCalibration(), and CalibrationProbPoissonRisk, for the evaluation (RRPlot), and calibration of cox models (CoxRiskCalibration) or logistic models (CalibrationProbPoissonRisk) of survival data. Furthermore, it can be used to evaluate any Risk index that rerun...
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Feature Discovery and Decorrelation
1 Effect of GDSTM-Based Decorrelation on Feature Discovery Here I showcase of to use BSWiMS feature selection/modeling function coupled with Goal Driven Sparse Transformation Matrix (GDSTM) as a pre-processing step to decorrelate highly correlated features. The aim(s) are: To improve model performance by uncovering the hidden information be...
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FDeA and Machine Learning on the Vehicle data set
FDeA-Based Decorrelation on Vehicle This document describes the use of the FRESA.CAD::GDSTMDecorrelation() function that runs the feature Decorrelation analysis (FDeA) algorithm. This demo uses FRESA.CAD and mlbench R packages: knitr::opts_chunk$set(collapse = TRUE, warning = FALSE, message = FALSE,comment = "#>") library("FRESA.CAD") ## Load...
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GDSTM Machine Learning on the Sonar Data Set
Filtered ML fit and the GDSTM with FRESA.CAD Here we make use of the FRESA.CAD::filteredfit() function to train ML models with and without GDSTM. Naive-Bayes (NB) and LASSO models are used in this demo. This scrip uses FRESA.CAD and mlbench R packages: knitr::opts_chunk$set(collapse = TRUE, warning = FALSE, message = FALSE,comment = "#>") libr...
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GDSTM and ARCENE
Filtered ML fit and the GDSTM with FRESA.CAD Here we make use of the FRESA.CAD::filteredfit() function to train ML models with and without GDSTM on the ARCENE data set. Isabelle Guyon, Steve R. Gunn, Asa Ben-Hur, Gideon Dror, 2004. Result analysis of the NIPS 2003 feature selection challenge. In: NIPS. \[Web Link\]. from: https://archive.ics....
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Multiple Features and the FDeA
GDSTM Decorrelation on the multiple features Dataset This document describes the use of the FRESA.CAD::GDSTMDecorrelation() function that runs the feature correlation analysis (FDeA) algorithm on the mfeat data set: https://archive.ics.uci.edu/ml/datasets/Multiple+Features M. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handw...
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