boot.resample {multtest} | R Documentation

## Non-parametric bootstrap resampling function in package 'multtest'

### Description

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`.

### Usage

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

### Arguments

`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.

### Value

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.

### Author(s)

Katherine S. Pollard, http://lowelab.ucsc.edu/katie/
with design contributions from Sandrine Dudoit and Mark J. van der Laan.

### References

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

### See Also

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

### Examples

    #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)

[Package multtest version 1.6.0 Index]