Supported by Dr. Osamu Ogasawara and  providing  . |
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Last data update: 2014.03.03 |
Examples of fitting models, predicting class labels, evaluating prediction, and analyzing fitting resultsDescriptionThese examples demonstrate how to use BCBCSF package. They use
all prior and Markov chain sampling settings by default (except
ReferencesLi, 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
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.
##}
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(BCBCSF)
Loading required package: abind
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/BCBCSF/example.Rd_%03d_medium.png", width=480, height=480)
> ### Name: d1:bcbcsfexamples
> ### Title: Examples of fitting models, predicting class labels, evaluating
> ### prediction, and analyzing fitting results
> ### Aliases: bcbcsfexamples
>
> ### ** 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)
Be Patient ... BCBCSF is fitting...
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> ## 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)
$probs_at_truelabels
fsel1 fsel2
Case1 1.0000000 1.0000000
Case2 1.0000000 1.0000000
Case3 1.0000000 1.0000000
Case4 1.0000000 1.0000000
Case5 1.0000000 1.0000000
Case6 0.9775752 0.9999999
Case7 0.9864736 0.9999997
Case8 0.2664777 0.6955399
Case9 0.5488585 0.7758773
$summary
No.Features Error.Rate AMLP
fsel1 20 0.1111111 0.21763089
fsel2 50 0.0000000 0.06853646
>
> ##########################################################################
> ####################### visualizing prediction results ###################
> ##########################################################################
> ## reload one bcbcsf fit result from hardrive
> fit_bcbcsf <- reload_fit_bcbcsf (out_fit$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 )
Fold 1: Be Patient ... BCBCSF is fitting...
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Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and