sfa_itemplot: 2-D Item Map (t-SNE, UMAP, PCA, or MDS)

In semanticfa: Semantic Factor Analysis of Language Model Embeddings

2-D Item Map (t-SNE, UMAP, PCA, or MDS)

Description

A 2-D scatter of the scale's items, the embedding-space companion to sfa_corplot: each point is an item, points are coloured by their theoretical factor and labelled with their short code, so you can see at a glance which items cluster together, which sit between constructs, and which are outliers. Operates on the same (transformed) item embeddings the factor analysis uses, or on a similarity matrix (converted to a distance).

Usage

sfa_itemplot(
  x,
  method = c("tsne", "umap", "pca", "mds"),
  factors = NULL,
  labels = NULL,
  color = TRUE,
  perplexity = NULL,
  n_neighbors = NULL,
  seed = 42,
  pch = 19,
  cex = 0.9,
  legend = TRUE,
  ...
)

sfa_tsneplot(x, method = c("tsne", "umap", "pca", "mds"), ...)

Arguments

x	An "sfa" object (uses its item embeddings) or a symmetric numeric item-by-item similarity matrix.
method	Projection: "tsne" (default), "umap", "pca", or "mds" (classical multidimensional scaling). All four work out of the box (Rtsne and uwot are dependencies; PCA and MDS are base R). t-SNE and UMAP are better at showing local clusters but are only sensible above a handful of items.
factors, labels	Optional per-item factor labels and point labels (codes). Default to those carried on x (or the matrix's "factors"/"codes" attributes).
color	Logical; colour points by factor (default TRUE).
perplexity	t-SNE perplexity (method = "tsne"). If NULL, a safe value is chosen for the item count (max(1, min(30, floor((n - 1) / 3)))).
n_neighbors	UMAP neighbourhood size (method = "umap"). If NULL, min(15, n - 1).
seed	Random seed for reproducibility (t-SNE and UMAP are stochastic).
pch, cex	Point symbol and size.
legend	Logical; draw a factor legend (default TRUE).
...	Passed to plot.

Details

The projection method is selectable via method; method = "tsne" reproduces the original behaviour. sfa_tsneplot() is a deprecated alias kept for back-compatibility.

Value

Invisibly, a list with the 2-D coordinates Y, the factors, the labels, and the method used.

References

van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.

McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for dimension reduction. arXiv:1802.03426.

See Also

sfa_corplot

Examples

data(big5)
fit <- sfa(
  data.frame(code = big5$codes, item = big5$items,
             factor = big5$factors, scoring = big5$scoring),
  embeddings = big5$embeddings, scoring = big5$scoring, nfactors = 5)
sfa_itemplot(fit, method = "pca")    # runnable: bundled data, base-R PCA
## Not run: 
sfa_itemplot(fit)                    # t-SNE (default)
sfa_itemplot(fit, method = "umap")   # UMAP

## End(Not run)

semanticfa documentation built on Sept. 2, 2026, 1:07 a.m.