plot_profile
(Package: PRIMsrc) :
Visualization for Model Selection/Validation
Function for plotting the cross-validated tuning profiles of a PRSP object. It uses the user's choice of statistics among the Log Hazard Ratio (LHR), Log-Rank Test (LRT) or Concordance Error Rate (CER) as a function of the model tuning parameter, that is, the optimal number of peeling steps of the peeling sequence (inner loop of our PRSP algorithm).
plot_boxkm
(Package: PRIMsrc) :
Visualization of Survival Distributions
Function for plotting the cross-validated survival distributions of a PRSP object. Plot the cross-validated Kaplan-Meir estimates of survival distributions for the highest risk (inbox) versus lower-risk (outbox) groups of samples at each iteration of the peeling sequence (inner loop of our PRSP algorithm).
Real.2
(Package: PRIMsrc) :
Real Dataset #2: Genomic Dataset (eqn{p >> n
Publicly available lung cancer genomic data from the Chemores Cohort Study. This data is part of an integrated study of mRNA, miRNA and clinical variables to characterize the molecular distinctions between squamous cell carcinoma (SCC) and adenocarcinoma (AC) in Non Small Cell Lung Cancer (NSCLC) aside large cell lung carcinoma (LCC). Tissue samples were analysed from a cohort of 123 patients who underwent complete surgical resection at the Institut Mutualiste Montsouris (Paris, France) between 30 January 2002 and 26 June 2006. In this genomic dataset, the expression levels of Agilent miRNA probes (p=939) were included from the n=123 samples of the Chemores cohort. The data contains normalized expression levels. See below the paper by Lazar et al. (2013) and Array Express data repository for complete description of the samples, tissue preparation, Agilent array technology, data normalization, etc. This dataset represents a situation where the number of covariates dominates the number of complete observations, or p >> n case.
● Data Source:
CranContrib
● Keywords: Real Dataset, Tumor sample comparisons
● Alias: Real.2
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plot_boxtraj
(Package: PRIMsrc) :
Visualization of Peeling Trajectories/Profiles
Function for plotting the cross-validated peeling trajectories/profiles of a PRSP object. Applies to the pre-selected covariates specified by user and all other statistical quantities of interest at each iteration of the peeling sequence (inner loop of our PRSP algorithm).
Main end-user function for fitting a cross-validated Survival Bump Hunting (SBH) model. Returns a cross-validated PRSP object, as generated by our Patient Recursive Survival Peeling or PRSP algorithm, containing cross-validated estimates of end-points statistics of interest.
Real.1
(Package: PRIMsrc) :
Real Dataset #1: Clinical Dataset (eqn{p < n
Publicly available HIV clinical data from the Women's Interagency HIV cohort Study (WIHS). Inclusion criteria of the study were that women at enrolment were (i) alive, (ii) HIV-1 infected, and (iii) free of clinical AIDS symptoms. Women were followed until the first of the following occurred: (i) treatment initiation (HAART), (ii) AIDS diagnosis, (iii) death, or administrative censoring. The studied outcomes were the competing risks "AIDS/Death (before HAART)" and "Treatment Initiation (HAART)". However, here, for simplification purposes, only the first of the two competing events (i.e. the time to AIDS/Death), was used in this dataset example. Likewise, the entire study enrolled 1164 women, but only the complete cases were used in this clinical dataset example for simplification. Variables included history of Injection Drug Use ("IDU") at enrollment, African American ethnicity ("Race"), age ("Age"), and baseline CD4 count ("CD4"). The question in this dataset example was whether it is possible to achieve a prognostication of patients for AIDS and HAART. See below Bacon et al. (2005) and the WIHS website for more details.
● Data Source:
CranContrib
● Keywords: AIDS Prognostication, Real Dataset
● Alias: Real.1
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Function for plotting the cross-validated covariates traces of a PRSP object. Plot the cross-validated modal trace curves of covariate importance and covariate usage of the pre-selected covariates specified by user at each iteration of the peeling sequence (inner loop of our PRSP algorithm).