boot.resample            package:multtest            R Documentation

_N_o_n-_p_a_r_a_m_e_t_r_i_c _b_o_o_t_s_t_r_a_p _r_e_s_a_m_p_l_i_n_g _f_u_n_c_t_i_o_n _i_n _p_a_c_k_a_g_e '_m_u_l_t_t_e_s_t'

_D_e_s_c_r_i_p_t_i_o_n:

     Given a data set and a closure, which consists of a function for
     computing the test statistic and its enclosing environment, this
     function produces a non-parametric bootstrap estimated test
     statistics null distribution. The observations in the data are
     resampled using the ordinary non-parametric bootstrap and used to
     produce an estimated test statistics distribution. This
     distribution is then centered and scaled to produce the null
     distribution. This function is called by 'MTP'.

_U_s_a_g_e:

     boot.resample(X, stat.closure, W = NULL, B = 1000, theta0 = 0, tau0 = 1, alternative = "two.sided")

_A_r_g_u_m_e_n_t_s:

       X: A matrix, data.frame or exprSet containing the raw data. In
          the case of an exprSet, 'exprs(X)' is the data of interest
          and 'pData(X)' may contain outcomes and covariates of
          interest. For currently implemented tests, one hypothesis is
          tested for each row of the data.

stat.closure: A closure for test statistic computation, like those
          produced internally by the 'MTP' function. The closure
          consists of a function for computing the test statistic and
          its enclosing environment, with bindings for relevant
          additional arguments (such as null values, outcomes, and
          covariates).

       W: A vector or matrix containing non-negative weights to be used
          in computing the test statistics. If a matrix, 'W' must be
          the same dimension as 'X' with one weight for each value in
          'X'. If a vector, 'W' may contain one weight for each
          observation (i.e. column) of 'X' or one weight for each
          variable (i.e. row) of 'X'. In either case, the weights are
          duplicated appropriately. Weighted f-tests are not available.
          Default is 'NULL'.

       B: The number of bootstrap iterations (i.e. how many resampled
          data sets) or the number of permutations (if 'nulldist' is
          'perm'). Can be reduced to increase the speed of computation,
          at a cost to precision. Default is 1000.

  theta0: The value used to center the test statistics. For tests based
          on a form of t-statistics, this should be zero (default). For
          f-tests, this should be 1.

    tau0: The value used to scale the test statistics. For tests based
          on a form of t-statistics, this should be 1 (default). For
          f-tests, this should be 2/(K-1), where K is the number of
          groups.

alternative: Character string indicating the alternative hypotheses, by
          default 'two.sided'. For one-sided tests, use 'less' or
          'greater' for null hypotheses of 'greater than or equal'
          (i.e. alternative is 'less') and 'less than or equal',
          respectively.

_V_a_l_u_e:

     A matrix of dimension number of hypotheses ('nrow(X)') by number
     of bootstrap iterations ('B'). This is the estimated joint test
     statistics null distribution. Each column is a centered and scaled
     resampled vector of test statistics. Each row is the bootstrap
     estimated marginal null distribution for a single hypothesis. This
     object is returned in slot 'nulldist' of an object of class 'MTP'
     when the argument 'keep.nulldist' to the 'MTP' function is TRUE.

_N_o_t_e:

     Thank you to Peter Dimitrov for suggestions about the code.

_A_u_t_h_o_r(_s):

     Katherine S. Pollard, <URL: http://lowelab.ucsc.edu/katie/>
      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>

_S_e_e _A_l_s_o:

     'MTP', 'MTP-class', 'get.Tn', 'ss.maxT', 'mt.sample.teststat'

_E_x_a_m_p_l_e_s:

     #data example: ALL data set
     set.seed(99)
     data<-matrix(rnorm(90),nr=9)

     #closure
     ttest<-meanX(psi0=0,na.rm=TRUE,standardize=TRUE,alternative="two.sided",robust=FALSE)

     #test statistics
     obs<-get.Tn(X=data,stat.closure=ttest,W=NULL)

     #bootstrap null distribution (B=100 for speed)
     nulldistn<-boot.resample(X=data,W=NULL,stat.closure=ttest,B=100,theta0=0,tau0=1,alternative="two.sided")

     #unadjusted p-values
     rawp<-apply((obs[1,]/obs[2,])<=nulldistn,1,mean)
     sum(rawp<=0.01)

