# Examples of fitting models, predicting class labels, evaluating prediction, and analyzing fitting results 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. ## See Also `bcbcsf_fitpred`, `bcbcsf_pred`, `cross_vld`, `eval_pred`, `reload_fit_bcbcsf`, `bcbcsf_sumfit`, `bcbcsf_plotsumfit` ## Examples ```r ##\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. ##} ``` ## References 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

example function