Package: RCASPAR 1.53.0

Douaa Mugahid

RCASPAR: A package for survival time prediction based on a piecewise baseline hazard Cox regression model.

The package is the R-version of the C-based software \bold{CASPAR} (Kaderali,2006: \url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine.

Authors:Douaa Mugahid, Lars Kaderali

RCASPAR_1.53.0.tar.gz
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RCASPAR.pdf |RCASPAR.html✨
RCASPAR/json (API)

# Install 'RCASPAR' in R:
install.packages('RCASPAR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • Bergamaschi - Gene expression data of 82 patients with 10 genes as covariates
  • survData - Survial data of 82 patients

On BioConductor:RCASPAR-1.53.0(bioc 3.21)RCASPAR-1.52.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

acghgeneexpressiongeneticsproteomicsvisualization

3.30 score 1 scripts 179 downloads 17 exports 0 dependencies

Last updated 22 days agofrom:105038a9b2. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winNOTEOct 31 2024
R-4.5-linuxNOTEOct 31 2024
R-4.4-winNOTEOct 31 2024
R-4.4-macNOTEOct 31 2024
R-4.3-winNOTEOct 31 2024
R-4.3-macNOTEOct 31 2024

Exports:deriv_weight_estimator_BLHderiv_weight_estimator_BLH_nopriorkmpltkmplt_svrllogrnkpltgammapltpriorsimpsonSTpredictor_BLHSTpredictor_xvBLHsurvivAURCsurvivROCtrapezoidweight_estimator_BLHweight_estimator_BLH_nopriorweights_BLHweights_xvBLH

Dependencies:

RCASPAR: Software for high-dimentional-data driven survival time prediction

Rendered fromRCASPAR.Rnwusingutils::Sweaveon Oct 31 2024.

Last update: 2013-11-01
Started: 2013-11-01

Readme and manuals

Help Manual

Help pageTopics
A package for survival time prediction based on a piecewise baseline hazard Cox regression model.RCASPAR-package RCASPAR
Gene expression data of 82 patients with 10 genes as covariatesBergamaschi
A function that gives the derivative of the objective function of the model for gradient-based optimization algorithms.deriv_weight_estimator_BLH
A function that gives the derivative of the objective function of the model for gradient-based optimization algorithms without including the prior on the regression coefficients.deriv_weight_estimator_BLH_noprior
Plot Kaplan Meier curvekmplt
A function that plots the KM curves of $2-3$ patient sets in one graph.kmplt_svrl
Performs Log Rank test on the long and short patient setslogrnk
Plotting the gamma distribution of shape parameterpltgamma
A function to visualize the shape of the prior on the weights with the chosen q and s parameters.pltprior
A function that calculates the area under a curve based on the Simposon algorithmsimpson
Predicts the survival times of the validation set based on the regression coefficients and baseline hazards determined according to the Piecewise baseline hazard Cox regression model.STpredictor_BLH
This function performs a cross validation on the full data set to help predict the survival times of the patients using the piecewise baseline hazard PH Cox model.STpredictor_xvBLH
Survial data of 82 patientssurvData
A function that calculates the area under a curve constructed from plotting the area under a ROC curve at the corresponding time point at which it was generated.survivAURC
Generates the ROC curve at a given time point given the observed and predicted survival data in the presence of censored subjects.survivROC
A function that calculates the area under a curve based on the Simposon algorithmtrapezoid
Returns the value of the objective function used for optimizing for the regression parameters and baseline hazards in the model.weight_estimator_BLH
Returns the value of the objective function used for optimizing for the regression parameters and baseline hazards in the model, without including the prior on the regression coefficients.weight_estimator_BLH_noprior
Optimization for the regression coefficients and baseline hazards that maximize the partial likelihood in our PW Cox PH regression model.weights_BLH
A special version of STpredictor.BLH used within k-xv to predict the survival times of the kth validation group in the cross validation step.weights_xvBLH