lda and qda objects are created from the MASS package, leveraged to carry out linear discriminant analysis and quadratic discriminant analysis, respectively.
Examples
library(MASS)
fit_da <- function(fit_fn) {
boop <- runif(1e6)
fit_fn(y ~ x, data.frame(y = rep(letters[1:4], 10000), x = rnorm(40000)))
}
lda_fit <- fit_da(lda)
qda_fit <- fit_da(qda)
lda_fit_b <- butcher(lda_fit)
qda_fit_b <- butcher(qda_fit)
weigh(lda_fit)
#> # A tibble: 9 × 2
#> object size
#> <chr> <dbl>
#> 1 terms 17.5
#> 2 call 0.00202
#> 3 means 0.00084
#> 4 scaling 0.000624
#> 5 prior 0.000496
#> 6 counts 0.00048
#> 7 lev 0.000304
#> 8 svd 0.000056
#> 9 N 0.000056
weigh(lda_fit_b)
#> # A tibble: 9 × 2
#> object size
#> <chr> <dbl>
#> 1 terms 0.00326
#> 2 call 0.00202
#> 3 means 0.00084
#> 4 scaling 0.000624
#> 5 prior 0.000496
#> 6 counts 0.00048
#> 7 lev 0.000304
#> 8 svd 0.000056
#> 9 N 0.000056
weigh(qda_fit)
#> # A tibble: 9 × 2
#> object size
#> <chr> <dbl>
#> 1 terms 17.5
#> 2 call 0.00202
#> 3 scaling 0.00162
#> 4 means 0.00084
#> 5 prior 0.000496
#> 6 counts 0.00048
#> 7 lev 0.000304
#> 8 ldet 0.00008
#> 9 N 0.000056
weigh(qda_fit_b)
#> # A tibble: 9 × 2
#> object size
#> <chr> <dbl>
#> 1 terms 0.00326
#> 2 call 0.00202
#> 3 scaling 0.00162
#> 4 means 0.00084
#> 5 prior 0.000496
#> 6 counts 0.00048
#> 7 lev 0.000304
#> 8 ldet 0.00008
#> 9 N 0.000056