evaluateParameters           package:macat           R Documentation

_E_v_a_l_u_a_t_e _P_e_r_f_o_r_m_a_n_c_e _o_f _K_e_r_n_e_l _P_a_r_a_m_e_t_e_r_s _b_y _C_r_o_s_s-_v_a_l_i_d_a_t_i_o_n

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

     For a given data set, chromosome, class, and kernel function, this
     function helps in determining optimal settings for the kernel
     parameter(s). The performance of individual parameter setting is
     assessed by cross- validation.

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

     evaluateParameters(data, class, chromosome, kernel, kernelparams = NULL,
                        paramMultipliers = 2^(-4:4), subset = NULL, 
                        newlabels = NULL, ncross = 10, verbose = TRUE)

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

    data: Gene expression data in the MACAT list format. See
          data(stjude) for an example.

   class: Sample class to be analyzed

chromosome: Chromosome to be analyzed

  kernel: Choose kernel to smooth scores along the chromose. Available
          are 'kNN' for k-Nearest-Neighbors, 'rbf' for
          radial-basis-function (Gaussian), 'basePairDistance' for a
          kernel, which averages over all genes within a given range of
          base pairs around a position.

kernelparams: Additional parameters for the kernel as list, e.g., 
          kernelparams=list(k=5) for taking the 5 nearest neighbours in
          the kNN-kernel. If NULL some defaults are set within the
          function.

paramMultipliers: Numeric vector. If you do cross-validation of the 
          kernel parameters, specify the multipliers of the given
          (standard) parameters to search over for the optimal one.

  subset: If a subset of samples is to be used, give vector of column-
          indices of these samples in the original matrix here.

newlabels: If other labels than the ones in the MACAT-list-structure
          are to be used, give them as character vector/factor here.
          Make sure argument 'class' is one of them.

  ncross: Integer. Specify how many folds in cross-validation.

 verbose: Logical. Should progress be reported to STDOUT?

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

     A list of class 'MACATevP' with 4 components: 

[parameterName]: List of assessed settings for the parameter
          [parameterName].

avgResid: Average Residual Sum of Suares for the parameter settings in
          the same order as the first component.

multiplier: Multiplier of the original parameters in the same order as
          the first components.

    best: List of parameter settings considered optimal by cross-
          validation. Can be directly inserted under the argument
          'kernelparams' of the 'evalScoring' function.

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

     MACAT development team

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

     'evalScoring'

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

     data(stjd)
     evalkNN6 <- evaluateParameters(stjd, class="T", chromosome=6,kernel=kNN, 
                                    paramMultipliers=c(0.01,seq(0.2,2.0,0.2),2.5))
     plot(evalkNN6)

