d1:bcbcsfexamples {BCBCSF} | R Documentation |
These examples demonstrate how to use BCBCSF package. They use
all prior and Markov chain sampling settings by default (except
no_rmc
as noted below). The methods for setting others can be found
from documents for specific functions. However, the default settings may
work well for a wide range of gene expression data.
Li, L. (2012), Bias-corrected Hierarchical Bayesian Classification with a Selected Subset of High-dimensional Features, Journal of American Statistical Association,107:497,120-134
bcbcsf_fitpred
, bcbcsf_pred
,
cross_vld
, eval_pred
,
reload_fit_bcbcsf
, bcbcsf_sumfit
,
bcbcsf_plotsumfit
##\dontrun{ ## load lymphoma microarray data data (lymphoma) ## select some cases as testing data set ts <- c (sort(sample (1:42,5)), 43:44, 61:62) ## training data X_tr <- lymph.X[-ts,] y_tr <- lymph.y[-ts] ## test data X_ts <- lymph.X[ts,] y_ts <- lymph.y[ts] ########################################################################## ######################## training and prediction ######################### ########################################################################## ## fitting training data with top features selected by F-statistic out_fit <- bcbcsf_fitpred (X_tr = X_tr, y_tr = y_tr, nos_fsel = c(20, 50), no_rmc = 100) ## note 1: if 'X_ts' is given above, prediction is made after fitting ## note 2: no_rmc = 100 is too small, omit it and use the default ## predicting class labels of test cases out_pred <- bcbcsf_pred (X_ts = X_ts, out_fit = out_fit) ## evaluate prediction given true labels eval_pred (out_pred = out_pred, y_ts = y_ts) ########################################################################## ####################### visualizing prediction results ################### ########################################################################## ## reload one bcbcsf fit result from hardrive fit_bcbcsf <- reload_fit_bcbcsf (out_fit$fitfiles[1]) ## the fitting result for no_fsel = 50 can be retrieved directly from ## out_fit: fit_bcbcsf_fsel50 <- out_fit$fit_bcbcsf ## summarize the fitting result sum_fit <- bcbcsf_sumfit (fit_bcbcsf) ## visualize fitting result bcbcsf_plotsumfit (sum_fit) ########################################################################## ############################ cross validation ############################ ########################################################################## ## doing cross validation with bcbcsf_fitpred on lymphoma data cv_pred <- cross_vld ( ##################### classifier, data, and fold ################### fitpred_func = bcbcsf_fitpred, X = lymph.X, y = lymph.y, nfold = 2, ################ all other arguments passed classifier ############ nos_fsel = c(20,50), no_rmc = 100 ) ## note: no_rmc = 100 is too small, omit it and use the default in practice ## evaluate prediction given true labels eval_pred (out_pred = cv_pred, y_ts = lymph.y) ## warning: this function is slow if nfold is large; if you have a ## computer cluster, you better parallel the cross validation folds. ##}