runSparseLogreg         package:SparseLogReg         R Documentation

_R _i_n_t_e_r_f_a_c_e _f_o_r _S_p_a_r_s_e_L_O_G_R_E_G

_D_e_s_c_r_i_p_t_i_o_n:

     This is a simplistic interface to SparseLOGREG

_U_s_a_g_e:

     runSparseLogreg(numTrains=62, numGenes=2000, numExperiments=100,
                     gammaMin=0.01, gammaMax=4.0, numGamma=5,
                     intKfold=3, tol=1e-6, maxFeatures=20, 
                     inData, inClass, ...)

_A_r_g_u_m_e_n_t_s:

numTrains: Number of training cases

numGenes: Number of variables/genes

numExperiments: Number of measurements/experiments

gammaMin: 

gammaMax: 

numGamma: number of Gamma

intKfold: number of internal k-folds

     tol: tolerance

maxFeatures: 

  inData: Input data matrix

 inClass: Classification vector (consisting of 'c(0,1)'

     ...: additional arguments are piped through to subfunctions

_V_a_l_u_e:

     out: result matrix of SparseLOGREG

_A_u_t_h_o_r(_s):

     M. T. Mader (interface),

_R_e_f_e_r_e_n_c_e_s:

     Shevade, S. K. and Keerthi, S. S. (2003): A simple and efficient
     algorithm for gene selection using sparse logistic regression.-
     Bioinformatics 19(17): 2246-2253

_S_e_e _A_l_s_o:

_E_x_a_m_p_l_e_s:

