bcbcsf_sumfit

reload_fit_bcbcsf

bcbcsf_plotsumfit

a string of name of a file saving a Markov chain fitting result; it can be found from the value <code>fitfiles</code> of function <code>bcbcsf_fitpred</code>.

fit_bcbcsf_afile

a list of Markov chain fitting result, returned by function <code>reload_fit_bcbcsf</code> and <code><a rd-options="" href="/link/bcbcsf_fitpred?package=BCBCSF&version=1.0-1" data-mini-rdoc="BCBCSF::bcbcsf_fitpred">bcbcsf_fitpred</a></code>; if it is NULL, it will be retrieved by running <code>reload_fit_bcbcsf</code> with value in <code>fit_bcbcsf_afile</code>.

fit_bcbcsf

<code>burn</code> of Markov chain (super)iterations will be discarded (burned) for evaluation, and only every <code>thin</code>th are used; by default, 20% of (super)iterations are burned, and <code>thin</code>=1.

burn, thin

a list returned by function <code>bcbcsf_sumfit</code>

sum_fit


These functions are used to look at the fitting results, especially plot the gene signals. 


d4:analyzefit

Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.

d4:analyzefit: Functions for analyzing and visualizing a BCBCSF fitting result

Description

Usage

Arguments

Value