Returns a tibble that extends the result of
Stats_trees with information about the fit models.
Compared to summaries returns by other Stats_* functions,
the information from this one is precomputed.
Stats_fits(x, patients = x$patients)
| x | A REVOLVER cohort where fits have been computed. |
|---|---|
| patients | The patients for which the summaries are required. |
A tibble with the fits stastics.
Other Summary statistics:
DET_index(),
Stats_cohort(),
Stats_drivers(),
Stats_trees(),
Stats()
# Data released in the 'evoverse.datasets' data('TRACERx_NEJM_2017_REVOLVER', package = 'evoverse.datasets') # Get the stats for all patients Stats_fits(TRACERx_NEJM_2017_REVOLVER)#> # A tibble: 99 x 9 #> patientID hasTrees numTrees maxScore minScore combInfTransf Solution #> <chr> <lgl> <int> <dbl> <dbl> <int> <int> #> 1 CRUK0001 TRUE 3 0.111 0.111 3 1 #> 2 CRUK0002 TRUE 2 0.75 0.0833 2 1 #> 3 CRUK0003 TRUE 1 1 1 1 1 #> 4 CRUK0004 TRUE 1 1 1 1 1 #> 5 CRUK0005 TRUE 1 1 1 1 1 #> 6 CRUK0006 TRUE 2 0.667 0.167 2 1 #> 7 CRUK0007 TRUE 1 1 1 1 1 #> 8 CRUK0008 TRUE 1 1 1 1 1 #> 9 CRUK0009 TRUE 1 1 1 1 1 #> 10 CRUK0010 TRUE 1 1 1 1 1 #> # … with 89 more rows, and 2 more variables: converged <lgl>, penalty <dbl># And subset the patients Stats_fits(TRACERx_NEJM_2017_REVOLVER, patients = c('CRUK0001', 'CRUK0002'))#> # A tibble: 2 x 9 #> patientID hasTrees numTrees maxScore minScore combInfTransf Solution converged #> <chr> <lgl> <int> <dbl> <dbl> <int> <int> <lgl> #> 1 CRUK0001 TRUE 3 0.111 0.111 3 1 TRUE #> 2 CRUK0002 TRUE 2 0.75 0.0833 2 1 TRUE #> # … with 1 more variable: penalty <dbl>