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sprint (version 1.0.4)

pboot: Parallel Bootstrapping

Description

pboot() generates R bootstrap replicates of a statistic applied to data. It implements a parallel version of the bootstrapping method boot() from the boot R package.

Arguments

data
array of data, if a 2D array then each row is considered as one multivariate observation
statistic
function, when sim is set to parametric, the first argument to statistic must be the data. For each replicate a simulated dataset returned by ran.gen will be passed. In all other cases,
R
number of bootstrap replicates
sim
string, indicates the type of simulation. The default value is "ordinary". Other possible values are parametric, balanced, permutation, and antithetic. Importance resampling is specified by including impor
stype
string, indicates what the second argument of statistic represents. The default value is i for indices. Other possible values are f for frequencies and w for weights. It is not used when sim is set t
strata
vector of integer, specifies the strata for multi-sample problems. This may be specified for any simulation, but is ignored when sim is set to parametric. When strata is supplied for a nonparamet
L
vector of influence values evaluated at the observations. This is used only when sim is set to antithetic. If not supplied, they are calculated through a call to empinf. This will use the infinitesimal jackknife
m
the number of predictions which are to be made at each bootstrap replicate. This is most useful for (generalized) linear models. This can only be used when sim is ordinary. m will usually be a single integer but
weights
array of importance weights. If a vector then it should have as many elements as there are observations in the input data. When simulation from more than one set of weights is required, weight
ran.gen
function, used only when sim is set to parametric. It describes how random values are to be generated. It should be a function of two arguments. The first argument should be the observed data a
mle
secong argument to ran.gen, typically these will be maximum likelihood estimates of the parameters. For efficiency mle is often a list containing all of the objects needed by ran.gen which can be calculat
simple
boolean, can only be set to TRUE if sim is set to ordinary, stype is set to I and n is set to 0. Otherwise it is ignored and generates a warning. By default a n by R index array is created which c
...
other named arguments for statistic which are passed unchanged each time.

Details

This version is an early but fully working prototype. However, it is not compatible with other SPRINT functions, i.e. you cannot bootstrap other parallel functions from the SPRINT library. It is therefore recommended to use it only as a standalone function.

See Also

boot SPRINT