d1:bcbcsfexamples {BCBCSF}

Examples of fitting models, predicting class labels, evaluating prediction, and analyzing fitting results

Description

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.

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

See Also

bcbcsf_fitpred, bcbcsf_pred, cross_vld, eval_pred, reload_fit_bcbcsf, bcbcsf_sumfit, bcbcsf_plotsumfit

Examples

##\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.
##}