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The function serves as a simplified alternative to the function boot from the library boot.
bootstrap(data, statistic, R = 1000, prob = NULL, matrix = FALSE)Raw data to be bootstrapped. A vector or quantitative data or a matrix if matrix =TRUE.
A function whose output is a statistic (e.g. a sample mean). The function must have only one argument, a call to data.
The number of bootstrap iterations.
A vector of probability weights for paramteric bootstrapping.
A logical statement. If matrix = TRUE then rows in the matrix are sampled as multivariate observations.
Returns a list. The utility function asbio:::print.bootstrap returns summary output. Invisible items include the resampling distribution of the statistic, the data, the statistic, and the bootstrap samples.
With bootstrapping we sample with replacement from a dataset of size n with n samples R times. At each of the R iterations a statistical summary can be created resulting in a bootstrap distribution of statistics.
Manly, B. F. J. (1997) Randomization and Monte Carlo Methods in Biology, 2nd edition. Chapman and Hall, London.
# NOT RUN {
data(vs)
# A partial set of observations from a single plot for a Scandinavian
# moss/vascular plant/lichen survey.
site18<-t(vs[1,])
#Shannon-Weiner diversity
SW<-function(data){
d<-data[data!=0]
p<-d/sum(d)
-1*sum(p*log(p))
}
bootstrap(site18[,1],SW,R=1000,matrix=FALSE)
# }
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