View source: R/SalienceContrastGen.R
SalienceContrastGen | R Documentation |
Given a list of vectors of Smith's S estimates, calculate contrasts between each item. Contrasts are either on the absolute difference scale (e.g., a 0.2 unit difference in Smith's S estimates) or on the ratio scale (e.g., item x has double the Smith's S cultural salience of item y). The input to this function is the output to commands 'SalienceBoot()', 'SalienceZOIB()' or 'SalienceOrdBeta()'.
SalienceContrastGen(data, contrast = "absolute_diff")
data |
This is a list of vectors of Smith's S estimates for a range of items items (i.e., commands 'SalienceBoot()', 'SalienceZOIB()' or 'SalienceOrdBeta()'). |
contrast |
String to denote whether to calculate contrasts on the absolute difference scale ("absolute_diff"; e.g., a 0.2 unit difference in Smith's S estimates) or on the ratio scale ("ratio_diff"; e.g., item x has double the Smith's S cultural salience of item y). Absolute difference is the default. |
A list of vectors for the contrasts in Smith's S between each item.
Daniel Major-Smith. <dan.major-smith@cas.au.dk>
Benjamin Grant Purzycki. <bgpurzycki@cas.au.dk>
## 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 6 items
## in terms of Smith's S, using 1,000 iterations for each item
S_boot <- SalienceBoot(fakeData.sal0, var_sel = "TOP", top = 6,
iterations = 1000, seed = 182, IDs_first = TRUE)
## Contrasts between each item (on absolute difference scale)
S_contrasts <- SalienceContrastGen(S_boot, contrast = "absolute_diff")
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