# mp_bootstrap

##### Compute bootstrap distributions for scaling functions

Bootstrapping of distributions of scaling functions as described by Benoit, Mikhaylov, and Laver (2009). Given a dataset with percentages of CMP categories, for each case the distribution of categories is resampled from a multinomial distribution and the scaling function computed for the resampled values. Arbitrary statistics of the resulting bootstrap distribution can be returned, such as standard deviation, quantiles, etc.

##### Usage

```
mp_bootstrap(data, fun = rile,
col_filter = "per((\\d{3}(_\\d)?)|\\d{4}|(uncod))",
statistics = list(sd), N = 1000, ...)
```

##### Arguments

- data
A data.frame with cases to be scaled and bootstrapped

- fun
function of a data row the bootstraped distribution of which is of interest

- col_filter
Regular expression matching the column names that should be permuted for the resampling (usually and by default ther per variables)

- statistics
A list (!) of statistics to be computed from the bootstrap distribution; defaults to standard deviation (

`sd`

). Must be functions or numbers, where numbers are interpreted as quantiles.- N
number of resamples to use for bootstrap distribution

- ...
more arguments passed on to

`fun`

##### References

Benoit, K., Laver, M., & Mikhaylov, S. (2009). Treating Words as Data with Error: Uncertainty in Text Statements of Policy Positions. American Journal of Political Science, 53(2), 495-513. http://doi.org/10.1111/j.1540-5907.2009.00383.x

*Documentation reproduced from package manifestoR, version 1.2.4, License: GPL (>= 3)*