# bootstrap_moby: Example bootstrap results for the full Moby Dick data set In csgillespie/poweRlaw: Analysis of Heavy Tailed Distributions

## Description

To explore the uncertainity in the model fit, this package provides a `bootstrap` function.

bootstrap_moby

The output from running 5000 bootstraps on the full Moby Dick data set (for a discrete power law) using the `bootstrap` function.

bootstrap_p_moby

The output from running 5000 bootstraps on the full Moby Dick data set (for a discrete power law) using the `bootstrap_p` function.

The `bootstrap_moby` values correspond to the first row of table 6.1 in the Clauset et al paper:

`bootstrap_moby\$gof`

the K-S statistic

`bootstrap_moby\$bootstraps`

a data frame for the optimal values from the bootstrapping procedure. Column 1: K-S, Column 2: xmin, Column 3: alpha. So standard deviation of column 2 and 3 is 2.2 and 0.033 (the paper gives 2 and 0.02 respectively).

The `bootstrap_p_moby` gives the p-value for the hypothesis test of whether the data follows a power-law. For this simulation study, we get a value of 0.43 (the paper gives 0.49).

A list

## Source

M. E. J. Newman, "Power laws, Pareto distributions and Zipf's law." Contemporary Physics 46, 323 (2005). See http://tuvalu.santafe.edu/~aaronc/powerlaws/data.htm for further details.

`moby`, `bootstrap`, `bootstrap_p`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## Generate the bootstrap_moby data set ## Not run: data(moby) m = displ\$new(moby) bs = bootstrap(m, no_of_sims=5000, threads=4, seed=1) ## End(Not run) #' ## Generate the bootstrap_p_moby data set ## Not run: bs_p = bootstrap_p(m, no_of_sims=5000, threads=4, seed=1) ## End(Not run) ```