Nothing
test_that("sfa_redundancy flags an injected duplicate pair", {
data(big5)
emb <- big5$embeddings
emb[2, ] <- emb[1, ] # make items 1 and 2 identical
df <- data.frame(code = big5$codes, item = big5$items, factor = big5$factors,
scoring = big5$scoring, stringsAsFactors = FALSE)
fit <- suppressWarnings(suppressMessages(
sfa(df, embeddings = emb, scoring = big5$scoring, nfactors = 5, seed = 42L)))
rd <- sfa_redundancy(fit, threshold = 0.95, method = "cosine")
expect_s3_class(rd, "sfa_redundancy")
pair_codes <- c(rd$pairs$item_i, rd$pairs$item_j)
expect_true(all(big5$codes[1:2] %in% pair_codes))
# one of the twins should be suggested for removal
expect_true(any(big5$codes[1:2] %in% rd$suggest_remove))
})
test_that("wto and cosine methods both run and return clusters", {
fit <- local({
data(big5)
df <- data.frame(code = big5$codes, item = big5$items, factor = big5$factors,
scoring = big5$scoring, stringsAsFactors = FALSE)
suppressWarnings(suppressMessages(
sfa(df, embeddings = big5$embeddings, scoring = big5$scoring,
nfactors = 5, seed = 42L)))
})
rc <- sfa_redundancy(fit, threshold = 0.5, method = "cosine")
expect_true(is.list(rc$clusters))
expect_true(is.data.frame(rc$pairs))
skip_if_not_installed("EGAnet") # wto sparsifies via EGAnet (faithful UVA)
rw <- sfa_redundancy(fit, threshold = 0.25, method = "wto")
expect_s3_class(rw, "sfa_redundancy")
expect_true(is.list(rw$clusters))
})
test_that("a square similarity matrix can be passed directly", {
data(big5)
sim <- sfa_similarity(big5$embeddings, encoding = "atomic_reversed",
scoring = big5$scoring)
attr(sim, "transformed_embeddings") <- NULL
rownames(sim) <- colnames(sim) <- big5$codes
rd <- sfa_redundancy(sim, threshold = 0.7, method = "cosine")
expect_s3_class(rd, "sfa_redundancy")
})
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