LassoCMA                 package:CMA                 R Documentation

_L_1 _p_e_n_a_l_i_z_e_d _l_o_g_i_s_t_i_c _r_e_g_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     The Lasso (Tibshirani, 1996) is one of the most popular tools for
     simultaneous shrinkage and variable selection. Recently,
     Young-Park and Hastie (2007) have developped and algorithm to
     compute the entire solution path of the Lasso for an arbitrary
     generalized linear model, implemented in the package 'glmpath'.
     The method can be used for variable selection alone, s.
     'GeneSelection'.

     For 'S4' method information, see 'LassoCMA-methods'.

_U_s_a_g_e:

     LassoCMA(X, y, f, learnind, norm.fraction = 0.1, ...)

_A_r_g_u_m_e_n_t_s:

       X: Gene expression data. Can be one of the following:

             *  A 'matrix'. Rows correspond to observations, columns to
                variables.

             *  A 'data.frame', when 'f' is _not_ missing (s. below).

             *  An object of class 'ExpressionSet'. *note*: by default,
                the predictors are scaled to have unit variance and
                zero mean. Can be changed by passing 'standardize =
                FALSE' via the '...' argument. 

       y: Class labels. Can be one of the following:

             *  A 'numeric' vector.

             *  A 'factor'.

             *  A 'character' if 'X' is an 'ExpressionSet' that
                specifies the phenotype variable.

             *  'missing', if 'X' is a 'data.frame' and a proper
                formula 'f' is provided.

          *WARNING*: The class labels will be re-coded to range from
          '0' to 'K-1', where 'K' is the total number of different
          classes in the learning set. 

       f: A two-sided formula, if 'X' is a 'data.frame'. The left part
          correspond to class labels, the right to variables.

learnind: An index vector specifying the observations that belong to
          the learning set. May be 'missing'; in that case, the
          learning set consists of all observations and predictions are
          made on the learning set.

norm.fraction: L1 Shrinkage intensity, expressed as the fraction of the
          coefficient L1 norm compared to the maximum possible L1 norm
          (corresponds to 'fraction = 1'). Lower values correspond to
          higher shrinkage. Note that the default (0.1) need not
          produce good results, i.e. tuning of this parameter is
          recommended.

     ...: Further arguments passed to the function 'glmpath' from the
          package of the same name.

_V_a_l_u_e:

     An object of class 'clvarseloutput'.

_N_o_t_e:

     For a strongly related method, s. 'ElasticNetCMA'.

     Up to now, this method can only be applied to binary
     classification.

_A_u_t_h_o_r(_s):

     Martin Slawski martin.slawski@campus.lmu.de

     Anne-Laure Boulesteix <URL: http://www.slcmsr.net/boulesteix>

_R_e_f_e_r_e_n_c_e_s:

     Tibshirani, R. (1996)

     Regression shrinkage and selection via the lasso.

     _Journal of the Royal Statistical Society B, 58(1), 267-288_

     Young-Park, M., Hastie, T. (2007)

     L1-regularization path algorithm for generalized linear models.

     _Journal of the Royal Statistical Society B, 69(4), 659-677_

_S_e_e _A_l_s_o:

     'compBoostCMA', 'dldaCMA', 'ElasticNetCMA', 'fdaCMA', 'flexdaCMA',
     'gbmCMA', 'knnCMA', 'ldaCMA', 'nnetCMA', 'pknnCMA', 'plrCMA',
     'pls_ldaCMA', 'pls_lrCMA', 'pls_rfCMA', 'pnnCMA', 'qdaCMA',
     'rfCMA', 'scdaCMA', 'shrinkldaCMA', 'svmCMA'

_E_x_a_m_p_l_e_s:

     ### load Golub AML/ALL data
     data(golub)
     ### extract class labels
     golubY <- golub[,1]
     ### extract gene expression
     golubX <- as.matrix(golub[,-1])
     ### select learningset
     ratio <- 2/3
     set.seed(111)
     learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
     ### run L1 penalized logistic regression (no tuning)
     lassoresult <- LassoCMA(X=golubX, y=golubY, learnind=learnind, norm.fraction = 0.2)
     show(lassoresult)
     ftable(lassoresult)
     plot(lassoresult)

