ar_project
generates a 2D projection of the target embedding.
Value
The function returns the associatoR
object with the
target_embeddings
overwritten by the projected embeddings.
Examples
ar_import(intelligence,
participant = participant_id,
cue = cue,
response = response,
participant_vars = c(gender, education),
response_vars = c(response_position, response_level)) %>%
ar_set_targets(targets = "cues") %>%
ar_embed_targets() %>%
ar_project_embedding()
#> 456 targets with count < min_count were dropped from embedding.
#>
#> ── An associatoR object ────────────────────────────────────────────────────────
#>
#> participants
#> # A tibble: 1,000 × 3
#> id gender education
#> <dbl> <chr> <chr>
#> 1 1 male high school
#> 2 2 male high school
#> 3 3 male high school
#> 4 4 male high school
#> 5 5 male high school
#> # ℹ 995 more rows
#>
#> cues
#> # A tibble: 804 × 1
#> cue
#> <chr>
#> 1 intelligence
#> 2 Einstein
#> 3 books
#> 4 IQ tests
#> 5 college
#> # ℹ 799 more rows
#>
#> responses
#> # A tibble: 29,882 × 5
#> id cue response response_position response_level
#> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 1 intelligence Einstein 1 1
#> 2 1 intelligence books 2 1
#> 3 1 intelligence IQ tests 3 1
#> 4 1 intelligence college 4 1
#> 5 1 intelligence smart people 5 1
#> # ℹ 29,877 more rows
#>
#> targets
#> # A tibble: 804 × 1
#> target
#> <chr>
#> 1 intelligence
#> 2 Einstein
#> 3 books
#> 4 IQ tests
#> 5 college
#> # ℹ 799 more rows
#>
#> target_embedding
#> # A tibble: 348 × 3
#> target dim_1 dim_2
#> <chr> <dbl> <dbl>
#> 1 intelligence -1.05 -0.327
#> 2 Einstein 0.293 -0.555
#> 3 books 0.926 0.0493
#> 4 IQ tests -2.35 0.0617
#> 5 college 1.89 -0.580
#> # ℹ 343 more rows
#>