boot.resample {multtest} | R Documentation |
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
.
boot.resample(X, stat.closure, W = NULL, B = 1000, theta0 = 0, tau0 = 1)
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. |
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.
Katherine S. Pollard, http://lowelab.ucsc.edu/katie/
with design contributions from Sandrine Dudoit and Mark J. van der Laan.
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). 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). 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). 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. http://www.bepress.com/ucbbiostat/paper121
MTP
, MTP-class
, get.Tn
, ss.maxT
, mt.sample.teststat
#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)