Description Usage Arguments Value
bcbcsf_fitpred
trains models with Gibbs sampling for each number of retained features. The results are saved in files. This function also makes predictions for test cases if they are provided.
bcbcsf_pred
uses the posterior samples saved by bcbcsf_fitpred
to predict the class labels of test cases. Prediction results are an array of predictive probabilities array_probs_pred
, whose rows for test cases, columns for classes, and the 3rd dimension for different numbers of retained features.
cross_vld
uses crossvalidation to obtain predictive probabilities for all cases of a data set. This generic function can be used with bcbcsf_fitpred
and other classifiers.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  bcbcsf_fitpred (
## arguments specifying info of data sets
X_tr, y_tr, nos_fsel = ncol (X_tr),
X_ts = NULL, standardize = FALSE, rankf = FALSE,
## arguments for prediction
burn = NULL, thin = 1, offset_sdxj = 0.5,
## arguments for Markov chain sampling
no_rmc = 1000, no_imc = 5, no_mhwmux = 10,
fit_bcbcsf_filepre = ".fitbcbcsf_",
## arguments specifying priors for parameters and hyerparameters
w0_mu = 0.05, alpha0_mu = 0.5, alpha1_mu = 3,
w0_x = 1.00, alpha0_x = 0.5, alpha1_x = 10,
w0_nu = 0.05, alpha0_nu = 0.5, prior_psi = NULL,
## arguments for metropolis sampling for wmu, wx
stepadj_mhwmux = 1, diag_mhwmux = FALSE,
## arguments for computing adjustment factor
bcor = 1, cut_qf = exp (10), cut_dpoi = exp (10), nos_sim = 1000,
## whether look at progress
monitor = TRUE)
bcbcsf_pred (X_ts, out_fit, burn = NULL, thin = 1, offset_sdxj = 0.5)
cross_vld (X, y, nfold = 10, folds = NULL,
fitpred_func = bcbcsf_fitpred, ...)

X_tr, X_ts, X 
matrices containing gene expression data; rows should be for the cases, and columns for different genes; 
y_tr,y 
class labels in training or test data set, or just a data set. 
nos_fsel 
a vector of numbers of features to be retained. 
burn,thin 

offset_sdxj 
a value between 0 and 1; 100* 
no_rmc, no_imc 

fit_bcbcsf_filepre 
a string added to the names of files saving Markov chain fitting results; the actual file names contain also the data dimension and number of retained features; when 
w0_mu,alpha0_mu,alpha1_mu,w0_x,alpha0_x,alpha1_x,w0_nu,alpha0_nu 
settings of priors for means and variances of genes; they are denoted by w_0^{μ}, α_1^{μ}, α_1^μ,w_0^x,α_0^x,α_1^x,w_0^ν,α_0^ν in the reference. 
prior_psi 
a vector of length the number of classes, specifying the Dirichlet prior distribution for probabilities of classes; it is denoted by c_{1:G} in the reference; by default, they are all equal to 1. 
no_mhwmux,stepadj_mhwmux, diag_mhwmux 
arguments specifying Metropolis sampling for \log(w^μ) and \log(w^x); respectively the number of iterations, stepsize adjustment, and an indicator representing whether one wants to pause and look into this sampling. 
bcor 
taking value 0 or 1, indicating whether biascorrection is to be applied. 
cut_qf, cut_dpoi,nos_sim 
arguments specifying approximation of adjustment factor; 
nfold, folds 

out_fit 
a list returned by 
standardize 
if it is set to TRUE, the original gene expression values are centralized and divided by the pooled standard deviation; by default, it is FALSE. 
rankf 
if it is set to TRUE, the original features will be reordered by Fstatistic; by default, it is FALSE. 
monitor 
if it is set to TRUE, progress of fitting is shown on screen 
fitpred_func 
an R function that can fit with training data, and predict for test data; the arguments of 
... 
arguments passed to classifier 
nos_fsel 
a vector of numbers of features retained. 
fitfiles 
a string vector of length 
array_probs_pred 
an array of predictive probabilities, whose rows for test cases, columns for classes, and the 3rd dimension for different numbers of retained features. 
fit_bcbcsf 
a list of Markov chain sampling results from the fitting with number of retained features equal to the last number in 
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