Summary statistics for the cohort's fits.

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)

Arguments

x

A REVOLVER cohort where fits have been computed.

patients

The patients for which the summaries are required.

Value

A tibble with the fits stastics.

See also

Other Summary statistics: DET_index(), Stats(), Stats_cohort(), Stats_drivers(), Stats_trees()

Examples

# 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 × 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
#> # ℹ 89 more rows
#> # ℹ 2 more variables: converged <lgl>, penalty <dbl>

# And subset the patients
Stats_fits(TRACERx_NEJM_2017_REVOLVER, patients = c('CRUK0001', 'CRUK0002'))
#> # A tibble: 2 × 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     
#> # ℹ 1 more variable: penalty <dbl>