Axing a coxph.

Axing a coxph.

Usage

# S3 method for coxph
axe_env(x, verbose = FALSE, ...)

# S3 method for coxph
axe_data(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 coxph object.

Details

The survival::coxph() model is unique in how it uses environments in its components, and butchering such an object can behave in surprising ways in any environment other than the global environment (such as when wrapped in a function). We do not recommend that you use butcher() with a coxph object anywhere other than the global environment.

Do this:

my_coxph_func <- function(df) {
    coxph(Surv(time, status) ~ x + strata(covar), df)
}
## in global environment only:
butcher(my_coxph_func(df))

Do not do this:

my_coxph_func <- function(df) {
    res <- coxph(Surv(time, status) ~ x + strata(covar), df)
    ## no:
    butcher(res)
}

## will not work correctly:
my_coxph_func(df)

Examples

library(survival)

example_data <-
  tibble::tibble(
    time = rpois(1000, 2) + 1,
    status = rbinom(1000, 1, .5),
    x = rpois(1000, .5),
    covar = rbinom(1000, 1, .5)
  )

example_data
#> # A tibble: 1,000 × 4
#>     time status     x covar
#>    <dbl>  <int> <int> <int>
#>  1     4      0     5     1
#>  2     2      1     0     1
#>  3     2      0     0     0
#>  4     3      0     0     0
#>  5     1      0     0     1
#>  6     4      1     1     0
#>  7     3      1     0     1
#>  8     1      1     1     0
#>  9     5      0     0     0
#> 10     3      1     0     1
#> # ℹ 990 more rows

make_big_model <- function() {
  boop <- runif(1e6)
  coxph(Surv(time, status) ~ x + strata(covar), example_data)
}

res <- make_big_model()

weigh(res)
#> # A tibble: 20 × 2
#>    object                    size
#>    <chr>                    <dbl>
#>  1 terms                 8.09    
#>  2 formula               8.09    
#>  3 y                     0.0177  
#>  4 residuals             0.00948 
#>  5 linear.predictors     0.00805 
#>  6 call                  0.00146 
#>  7 concordance           0.000752
#>  8 coefficients          0.00028 
#>  9 means                 0.00028 
#> 10 wald.test             0.00028 
#> 11 var                   0.000224
#> 12 xlevels.strata(covar) 0.000176
#> 13 method                0.000112
#> 14 loglik                0.000064
#> 15 score                 0.000056
#> 16 iter                  0.000056
#> 17 n                     0.000056
#> 18 nevent                0.000056
#> 19 assign.x              0.000056
#> 20 timefix               0.000056
weigh(butcher(res))
#> # A tibble: 20 × 2
#>    object                    size
#>    <chr>                    <dbl>
#>  1 terms                 8.09    
#>  2 residuals             0.00948 
#>  3 linear.predictors     0.00805 
#>  4 formula               0.00149 
#>  5 call                  0.00146 
#>  6 concordance           0.000752
#>  7 coefficients          0.00028 
#>  8 means                 0.00028 
#>  9 wald.test             0.00028 
#> 10 var                   0.000224
#> 11 xlevels.strata(covar) 0.000176
#> 12 method                0.000112
#> 13 loglik                0.000064
#> 14 score                 0.000056
#> 15 iter                  0.000056
#> 16 n                     0.000056
#> 17 nevent                0.000056
#> 18 assign.x              0.000056
#> 19 timefix               0.000056
#> 20 y                     0.000048