ethnobotanyR (version 0.1.9)

ethno_boot: Bootstrap analyses of ethnobotany indices

Description

Creates a non-parametric bootstrap as a Bayesian Model https://www.sumsar.net/blog/2015/04/the-non-parametric-bootstrap-as-a-bayesian-model/. This is meant to be applied for ethnobotany data and indices in the ethnobotanyR package. Performs a Bayesian bootstrap and returns a sample of size 'n1' representing the posterior distribution of the chosen statistic (i.e. 'mean'). The function returns a vector if the statistic is one-dimensional (like for mean(...)) or a data.frame if the statistic is multi-dimensional (like for the coefficients 'coefs.' of a regression model 'lm').

Usage

ethno_boot(data, statistic, n1 = 1000, 
n2 = 1000, ...)

Value

Bayesian bootstrap of chosen ethnobotany indices in ethnobotanyR package.

Arguments

data

Can be either a vector, matrix or a data.frame.

statistic

A function that accepts data as its first argument. Should return a numeric vector.

n1

The size of the bootstrap sample.

n2

The sample size used to calculate the statistic for each bootstrap draw.

...

Further arguments passed on to the statistic function.

Application

This function was inspired by Rasmus Bååth's “The Non-Parametric Bootstrap as a Bayesian Model” Publishable Stuff, 2015. https://www.sumsar.net/blog/2015/04/the-non-parametric-bootstrap-as-a-bayesian-model/. In order to understand the various possible applications of this function for ethnobotany analyses it is important to read through that work.

ethnobotanyR users often have a large number of counts in cells of the data set after categorization (i.e one user cites ten different ‘food’ uses but this is just one category). Most quantitative ethnobotany tools are not equipped for cases where the theoretical maximum number of use reports in one category, for one species by one informant is >1. This function and the ethno_bayes_consensus function may be useful to work with these richer datasets for the Bayes consensus analysis.

References

Bååth, Rasmus. “The Non-Parametric Bootstrap as a Bayesian Model” Publishable Stuff, 2015. https://www.sumsar.net/blog/2015/04/the-non-parametric-bootstrap-as-a-bayesian-model/. Rubin, Donald B. “The Bayesian Bootstrap.” Annals of Statistics 9, no. 1 (January 1981): 130–34. tools:::Rd_expr_doi("10.1214/aos/1176345338").

Examples

#Use built-in ethnobotany data example

ethno_boot(data = ethnobotanydata$Use_1, 
statistic = mean, n1 = 1000)

#Generate random dataset of three informants uses for four species

eb_data <- data.frame(replicate(10,sample(0:1,20,rep=TRUE)))
names(eb_data) <- gsub(x = names(eb_data), pattern = "X", replacement = "Use_")  
eb_data$informant<-sample(c('User_1', 'User_2', 'User_3'), 20, replace=TRUE)
eb_data$sp_name<-sample(c('sp_1', 'sp_2', 'sp_3', 'sp_4'), 20, replace=TRUE)

# Simple Bayesian bootstrap of the mean ####

boot_dataUR <- URs(eb_data)

ethno_boot(data = boot_dataUR$URs, statistic = mean)

# Generate random dataset of three informants with multiple uses for four species

eb_multi_use_data <- data.frame(replicate(10,sample(0:10,20,rep=TRUE)))
names(eb_multi_use_data) <- gsub(x = names(eb_multi_use_data), pattern = "X", replacement = "Use_")  
eb_multi_use_data$informant<-sample(c('User_1', 'User_2', 'User_3'), 20, replace=TRUE)
eb_multi_use_data$sp_name<-sample(c('sp_1', 'sp_2', 'sp_3', 'sp_4'), 20, replace=TRUE)

# Simple Bayesian bootstrap of the mean of Use_1 ####

ethno_boot(data = eb_multi_use_data$Use_1, statistic = mean)