MTP-class              package:multtest              R Documentation

_C_l_a_s_s "_M_T_P", _c_l_a_s_s_e_s _a_n_d _m_e_t_h_o_d_s _f_o_r _m_u_l_t_i_p_l_e _t_e_s_t_i_n_g _p_r_o_c_e_d_u_r_e _o_u_t_p_u_t

_D_e_s_c_r_i_p_t_i_o_n:

     An object of class MTP is the output of a particular multiple
     testing procedure, for example, generated by the MTP function. It
     has slots for the various data used to make multiple testing
     decisions, such as adjusted p-values and confidence regions.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form 
      new('MTP', 
      statistic = ...., object of class numeric
      estimate  = ...., object of class numeric
      sampsize  = ...., object of class numeric
      rawp      = ...., object of class numeric
      adjp      = ...., object of class numeric
      conf.reg  = ...., object of class array
      cutoff    = ...., object of class matrix
      reject    = ...., object of class matrix
      rawdist   = ...., object of class matrix
      nulldist  = ...., object of class matrix
      nulldist.type = ...., object of class character
      marg.null = ...., object of class character
      marg.par  = ...., object of class matrix
      label     = ...., object of class numeric
      index     = ...., object of class matrix
      call      = ...., object of class call
      seed      = ...., object of class integer
      )

_S_l_o_t_s:


     '_s_t_a_t_i_s_t_i_c' Object of class 'numeric', observed test statistics
          for each hypothesis, specified by the values of the 'MTP'
          arguments 'test', 'robust', 'standardize', and 'psi0'.

     '_e_s_t_i_m_a_t_e' For the test of single-parameter null hypotheses using
          t-statistics (i.e., not the F-tests), the numeric vector of
          estimated parameters corresponding to each hypothesis, e.g.
          means, differences in means, regression parameters.

     '_s_a_m_p_s_i_z_e' Object of class 'numeric', number of columns (i.e.
          observations) in the input data set.

     '_r_a_w_p' Object of class 'numeric', unadjusted, marginal p-values
          for each hypothesis.

     '_a_d_j_p' Object of class 'numeric', adjusted (for multiple testing)
          p-values for each hypothesis (computed only if the 'get.adjp'
          argument is TRUE).

     '_c_o_n_f._r_e_g' For the test of single-parameter null hypotheses using
          t-statistics (i.e., not the F-tests), the numeric array of
          lower and upper simultaneous confidence limits for the
          parameter vector, for each value of the nominal Type I error
          rate 'alpha' (computed only if the 'get.cr' argument is
          TRUE).

     '_c_u_t_o_f_f' The numeric matrix of cut-offs for the vector of test
          statistics for each value of the nominal Type I error rate
          'alpha' (computed only if the 'get.cutoff' argument is TRUE).

     '_r_e_j_e_c_t' Object of class ''matrix'', rejection indicators (TRUE
          for a rejected null hypothesis), for each value of the
          nominal Type I error rate 'alpha'.

     '_r_a_w_d_i_s_t' The numeric matrix for the estimated nonparametric
          non-null test statistics distribution (returned only if
          'keep.rawdist=TRUE' and if 'nulldist' is one of 'boot.ctr',
          'boot.cs', or 'boot.qt'). This slot must not be empty if one
          wishes to call 'update' to change choice of bootstrap-based
          null distribution.

     '_n_u_l_l_d_i_s_t' The numeric matrix for the estimated test statistics
          null distribution (returned only if 'keep.nulldist=TRUE');
          option not currently available for permutation null
          distribution, i.e.,  'nulldist='perm''). By default (i.e.,
          for 'nulldist='boot.cs''), the entries of 'nulldist' are the
          null value shifted and scaled bootstrap test statistics, with
          one null test statistic value for each hypothesis (rows) and
          bootstrap iteration (columns).

     '_n_u_l_l_d_i_s_t._t_y_p_e' Character value describing which choice of null
          distribution was used to generate the MTP results.  Takes on
          one of the values of the original 'nulldist' argument in the
          call to MTP, i.e., 'boot.cs', 'boot.ctr', 'boot.qt', 'ic', or
          'perm'.

     '_m_a_r_g._n_u_l_l' If 'nulldist='boot.qt'', a character value returning
          which choice of marginal null distribution was used by the
          MTP.  Can be used to check default values or to ensure manual
          settings were correctly applied.

     '_m_a_r_g._p_a_r' If 'nulldist='boot.qt'', a numeric matrix returning the
          parameters of the marginal null distribution(s) used by the
          MTP.  Can be used to check default values or to ensure manual
          settings were correctly applied.

     '_l_a_b_e_l' If 'keep.label=TRUE', a vector storing the values used in
          the argument 'Y'.  Storing this object is particularly
          important when one wishes to update MTP objects with
          F-statistics using default 'marg.null' and 'marg.par'
          settings when 'nulldist='boot.qt''. 

     '_i_n_d_e_x' For tests of correlation parameters a matrix corresponding
          to 't(combn(p,2))', where 'p' is the number of variables in
          'X'.  This matrix gives the indices of the variables
          considered in each pairwise correlation.  For all other
          tests, this slot is empty, as the indices are in the same
          order as the rows of 'X'.

     '_c_a_l_l' Object of class 'call', the call to the MTP function.

     '_s_e_e_d' An integer or vector for specifying the state of the random
          number generator used to create the resampled datasets. The
          seed can be reused for reproducibility in a repeat call to
          'MTP'. This argument is currently used only for the bootstrap
          null distribution (i.e., for 'nulldist="boot.xx"'). See
          '?set.seed' for details.

_M_e_t_h_o_d_s:

     'signature(x = "MTP")' 

     [ : Subsetting method for 'MTP' class, which operates selectively
          on each slot of an 'MTP' instance to retain only the data
          related to the specified hypotheses.

     _a_s._l_i_s_t : Converts an object of class 'MTP' to an object of class
          'list', with an entry for each slot.

     _p_l_o_t : plot methods for 'MTP' class, produces the following
          graphical summaries of the results of a MTP. The type of
          display may be specified via the 'which' argument. 

          1. Scatterplot of number of rejected hypotheses vs. nominal
          Type I error rate. 

          2. Plot of ordered adjusted p-values; can be viewed as a plot
          of Type I error rate vs. number of rejected hypotheses. 

          3. Scatterplot of adjusted p-values vs. test statistics (also
          known as "volcano plot"). 

          4. Plot of unordered adjusted p-values. 

          5. Plot of confidence regions for user-specified parameters,
          by default the 10 parameters corresponding to the smallest
          adjusted p-values  (argument 'top'). 

          6. Plot of test statistics and corresponding cut-offs (for
          each value of 'alpha') for user-specified hypotheses, by
          default the 10 hypotheses corresponding to the smallest
          adjusted p-values (argument 'top'). 

          The argument 'logscale' (by default equal to FALSE) allows
          one to use the negative decimal logarithms of the adjusted
          p-values in the second, third, and fourth graphical displays.
          The arguments 'caption' and 'sub.caption' allow one to change
          the titles and subtitles for each of the plots (default
          subtitle is the MTP function call). Note that some of these
          plots are implemented in the older function 'mt.plot'.

     _p_r_i_n_t : print method for 'MTP' class, returns a description of an
          object of class 'MTP', including sample size, number of
          tested hypotheses, type of test performed (value of argument
          'test'), Type I error rate (value of argument 'typeone'),
          nominal level of the test  (value of argument 'alpha'), name
          of the MTP  (value of argument 'method'), call to the
          function 'MTP'.

          In addition, this method produces a table with the class,
          mode, length, and dimension of each slot of the 'MTP'
          instance. 


     _s_u_m_m_a_r_y : summary method for 'MTP' class, provides numerical
          summaries of the results of a MTP and returns a list with the
          following three components. 

          1. rejections: A data.frame with the number(s) of rejected
          hypotheses for the nominal Type I error rate(s) specified by
          the 'alpha' argument of the function 'MTP'. (NULL values are
          returned if all three arguments 'get.cr', 'get.cutoff', and
          'get.adjp' are FALSE). 

          2. index: A numeric vector of indices for ordering the
          hypotheses according to first 'adjp', then 'rawp', and
          finally the absolute value of 'statistic' (not printed in the
          summary). 


          3. summaries: When applicable (i.e., when the corresponding
          quantities are returned by 'MTP'), a table with six number
          summaries of the distributions of the adjusted p-values,
          unadjusted p-values, test statistics, and parameter
          estimates.

     _u_p_d_a_t_e : update method for 'MTP' class, provides a mechanism to
          re-run the MTP with different choices of the following
          arguments - nulldist, alternative, typeone, k, q, fdr.method,
          alpha, smooth.null, method, get.cr, get.cutoff, get.adjp,
          keep.nulldist, keep.rawdist, keep.margpar. When evaluate is
          'TRUE', a new object of class MTP is returned. Else, the
          updated call is returned. The 'MTP' object passed to the
          update method must have either a  non-empty 'rawdist' slot or
          a non-empty 'nulldist' slot (i.e., must have been called with
          either 'keep.rawdist=TRUE' or 'keep.nulldist=TRUE').


          To save on memory, if one knows ahead of time that one will
          want to compare different choices of bootstrap-based null
          distribution, then it is both necessary and sufficient to
          specify 'keep.rawdist=TRUE', as there is no other means of
          moving between null distributions other than through the
          non-transformed non-parametric bootstrap distribution.  In
          this case, 'keep.nulldist=FALSE' may be used.  Specifically,
          if an object of class 'MTP' contains a non-empty 'rawdist'
          slot and an empty 'nulldist' slot, then a new null
          distribution will be generated according to the values of the
          'nulldist=' argument in the original call to 'MTP' or any
          additional specifications in the call to 'update'. On the
          other hand, if one knows that one wishes to only update an
          'MTP' object in ways which do not involve choice of null
          distribution, then 'keep.nulldist=TRUE' will suffice and
          'keep.rawdist' can be set to 'FALSE' (default settings).  The
          original null distribution object will then be used for all
          subsequent calls to 'update'. 

          N.B.: Note that 'keep.rawdist=TRUE' is only available for the
          bootstrap-based resampling methods.  The non-null
          distribution does not exist for the permutation or influence
          curve multivariate normal null distributions. 

     _m_t_p_2_e_b_m_t_p : coersion method for converting objects of class 'MTP'
          to objects of class 'EBMTP'.  Slots common to both objects
          are taken from the object of class 'MTP' and used to create a
          new object of class 'EBMTP'.  Once an object of class 'EBMTP'
          is created, one may use the method 'EBupdate' to perform
          resampling-based empirical Bayes multiple testing without the
          need for repeated resampling.

_A_u_t_h_o_r(_s):

     Katherine S. Pollard  and Houston N. Gilbert with design
     contributions from Sandrine Dudoit and Mark J. van der Laan.

_R_e_f_e_r_e_n_c_e_s:

     M.J. van der Laan, S. Dudoit, K.S. Pollard (2004), Augmentation
     Procedures for Control of the Generalized Family-Wise Error Rate
     and Tail Probabilities for the Proportion of False Positives,
     Statistical Applications in Genetics and Molecular Biology, 3(1). 
     <URL: http://www.bepress.com/sagmb/vol3/iss1/art15/>

     M.J. van der Laan, S. Dudoit, K.S. Pollard (2004), Multiple
     Testing. Part II. Step-Down Procedures for Control of the
     Family-Wise Error Rate, Statistical Applications in Genetics and
     Molecular Biology, 3(1). <URL:
     http://www.bepress.com/sagmb/vol3/iss1/art14/>

     S. Dudoit, M.J. van der Laan, K.S. Pollard (2004), Multiple
     Testing. Part I. Single-Step Procedures for Control of General
     Type I Error Rates, Statistical Applications in Genetics and
     Molecular Biology, 3(1). <URL:
     http://www.bepress.com/sagmb/vol3/iss1/art13/>

     Katherine S. Pollard and Mark J. van der Laan, "Resampling-based
     Multiple Testing: Asymptotic Control of Type I Error and
     Applications to Gene Expression Data" (June 24, 2003). U.C.
     Berkeley Division of Biostatistics Working Paper Series. Working
     Paper 121. <URL: http://www.bepress.com/ucbbiostat/paper121>

     M.J. van der Laan and A.E. Hubbard (2006), Quantile-function Based
     Null Distributions in Resampling Based Multiple Testing,
     Statistical Applications in Genetics and Molecular Biology, 5(1).
     <URL: http://www.bepress.com/sagmb/vol5/iss1/art14/>

     S. Dudoit and M.J. van der Laan.  Multiple Testing Procedures and
     Applications to Genomics.  Springer Series in Statistics.
     Springer, New York, 2008.

_S_e_e _A_l_s_o:

     'MTP', 'MTP-methods', 'EBMTP', 'EBMTP-methods', '[-methods',
     'as.list-methods', 'print-methods', 'plot-methods',
     'summary-methods', 'mtp2ebmtp',  'ebmtp2mtp'

_E_x_a_m_p_l_e_s:

     ## See MTP function: ? MTP

