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)

_A_r_g_u_m_e_n_t_s:

       X: A matrix or data.frame containing the raw data. 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.

_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.

_A_u_t_h_o_r(_s):

     Katherine S. Pollard, <URL: http://docpollard.com>
      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>

     Thank you to Peter Dimitrov for suggestions about the code.

_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)

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

