Axing a MASS discriminant analysis object.

lda and qda objects are created from the MASS package, leveraged to carry out linear discriminant analysis and quadratic discriminant analysis, respectively.

Usage

# S3 method for class 'lda'
axe_env(x, verbose = FALSE, ...)

# S3 method for class 'qda'
axe_env(x, verbose = FALSE, ...)

# S3 method for class 'polr'
axe_env(x, verbose = FALSE, ...)

Arguments

x

A model object.

verbose

Print information each time an axe method is executed. Notes how much memory is released and what functions are disabled. Default is FALSE.

...

Any additional arguments related to axing.

Value

Axed lda or qda object.

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   17.6     
#> 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   17.6     
#> 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

wrapped_polr <- function(fit_fn) {
  boop <- runif(1e6)
  fit <- fit_fn(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
  return(fit)
}
polr_fit <- wrapped_polr(polr)
polr_fit_b <- butcher(polr_fit)
weigh(polr_fit)
#> # A tibble: 26 × 2
#>    object             size
#>    <chr>             <dbl>
#>  1 terms         25.7     
#>  2 fitted.values  0.00369 
#>  3 lp             0.00177 
#>  4 call           0.0014  
#>  5 model.Sat      0.000984
#>  6 model.Type     0.000984
#>  7 model.Infl     0.00092 
#>  8 model.Cont     0.000848
#>  9 coefficients   0.000688
#> 10 niter          0.000392
#> # ℹ 16 more rows
weigh(polr_fit_b)
#> # A tibble: 26 × 2
#>    object            size
#>    <chr>            <dbl>
#>  1 terms         0.00437 
#>  2 fitted.values 0.00369 
#>  3 lp            0.00177 
#>  4 call          0.0014  
#>  5 model.Sat     0.000984
#>  6 model.Type    0.000984
#>  7 model.Infl    0.00092 
#>  8 model.Cont    0.000848
#>  9 coefficients  0.000688
#> 10 niter         0.000392
#> # ℹ 16 more rows