Pearson correlation between the proportions of topics over
documents (the "simple" method of stm::topicCorr()). Two topics that
tend to appear in the same documents correlate positively. Since the
proportions of a document sum to 1, most correlations are slightly
negative: read the positive ones, and the strongest negative ones.
Usage
topic_correlation(model, min_cor = NULL, ...)
Arguments
- model
A fitted stm model, or its document-topic matrix theta.
- min_cor
Optional numeric scalar: keep pairs whose correlation is
at least this value (e.g. 0.1).
- ...
Not used: an argument renamed in the development version of mintR gives an error
naming its replacement.
Value
A data.table with one row per pair of topics (topic_a <
topic_b) and correlation, by decreasing correlation.
Examples
theta <- matrix(
c(0.7, 0.2, 0.1, 0.1, 0.3, 0.6, 0.4, 0.4, 0.2, 0.2, 0.1, 0.7),
ncol = 3, byrow = TRUE
)
topic_correlation(list(theta = theta))
#> topic_a topic_b correlation
#> <int> <int> <num>
#> 1: 1 2 1.083368e-16
#> 2: 2 3 -4.385290e-01
#> 3: 1 3 -8.987170e-01
