SalienceEstimateSummary: SalienceEstimateSummary

In alastair-JL/AnthroTools: Some custom tools for anthropology

SalienceEstimateSummary

Description

Given a list of vectors of Smith's S estimates, summarise each vector in the list to calculate uncertainty intervals (e.g., percentile intervals from boot-strapping, or credible intervals from Bayesian models). The input to this function is the output to commands 'SalienceBoot()', 'SalienceZOIB()' or 'SalienceOrdBeta()'.

Usage

SalienceEstimateSummary(data, quantiles = c(0, 0.025, 0.1, 0.25, 0.5, 0.75, 0.9, 0.975, 1))

Arguments

data	This is a list of vectors of Smith's S estimates (i.e., the output of commands 'SalienceBoot()', 'SalienceZOIB()' or 'SalienceOrdBeta()').
quantiles	The specific quantiles/uncertainty intervals to estimate (default = c(0, 0.025, 0.1, 0.25, 0.5, 0.75, 0.9, 0.975, 1)).

Value

A data frame of Smith's S estimates and uncertainty intervals for each item.

Author(s)

Daniel Major-Smith. <[e-mail]>

Benjamin Grant Purzycki. <[e-mail]>

Examples

## Generate fake free-list data about fruits
set.seed(41)
fakeData <- GenerateFakeFreeListData() 

## Calculate item salience
fakeData.s <- CalculateSalience(fakeData, Subj = "Subj", Order = "Order", 
		CODE = "CODE", Salience = "CODE.S")

## Convert to data frame with maximum item saliences for each item as 
## separate rows, and including 0s
fakeData.sal0 <- FreeListTable(fakeData.s, Subj = "Subj", Order = "Order", 
		CODE = "CODE", Salience = "CODE.S", tableType = "MAX_SALIENCE")
head(fakeData.sal0)

## Calculate uncertainty in Smith's S via boot-strapping for top 8 items 
## in terms of Smith's S, using 1,000 iterations for each item
S_boot <- SalienceBoot(fakeData.sal0, var_sel = "TOP", top = 8, 
		iterations = 1000, seed = 182, IDs_first = TRUE)

## Summarise results, displaying median Smith's S estimate and 95% percentile intervals
res <- SalienceEstimateSummary(S_boot, quantiles = c(0.025, 0.5, 0.975))

alastair-JL/AnthroTools documentation built on June 10, 2025, 3:08 p.m.