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Apply a function over a list of models

Source: R/cfp_altapply.R
cfp_altapply.Rd

Apply a function to a list of cfp_pfres pr cfp_fgres objects stored in an cfp_altres object. This can be used to summarise alternate() results.

Usage

cfp_altapply(X, FUN, ...)

Arguments

X

Either a cfp_altres object or a list.

FUN

the function to be applied to each element of X: see ‘Details’. In the case of functions like +, %*%, the function name must be backquoted or quoted.

...

optional arguments to FUN.

Value

data.frame with the results of FUN bound together with added column run_id as identifier of the original list elements.

Examples

PROFLUX <- ConFluxPro::base_dat |> pro_flux()
model_list <- list('1' = PROFLUX, '2' = PROFLUX)

cfp_altapply(model_list, efflux)
#> 
#> A cfp_profile object 
#> id_cols: site Date gas 
#> 24  unique profiles 
#> 
#>      site       Date gas     efflux prof_id run_id
#> 1  site_a 2021-01-01 CO2  0.8347288       1      1
#> 2  site_b 2021-01-01 CO2  0.6741332       2      1
#> 3  site_a 2021-02-01 CO2  1.3672901       3      1
#> 4  site_b 2021-02-01 CO2  1.0613224       4      1
#> 5  site_a 2021-03-01 CO2  1.7443084       5      1
#> 6  site_b 2021-03-01 CO2  1.3906597       6      1
#> 7  site_a 2021-04-01 CO2  4.1451861       7      1
#> 8  site_b 2021-04-01 CO2  3.3212976       8      1
#> 9  site_a 2021-05-01 CO2  5.9120294       9      1
#> 10 site_b 2021-05-01 CO2  4.4778132      10      1
#> 11 site_a 2021-06-01 CO2  7.2487756      11      1
#> 12 site_b 2021-06-01 CO2  5.6816381      12      1
#> 13 site_a 2021-07-01 CO2 10.1179566      13      1
#> 14 site_b 2021-07-01 CO2  8.5211395      14      1
#> 15 site_a 2021-08-01 CO2  8.1122940      15      1
#> 16 site_b 2021-08-01 CO2  6.8952154      16      1
#> 17 site_a 2021-09-01 CO2  6.2227577      17      1
#> 18 site_b 2021-09-01 CO2  5.2422286      18      1
#> 19 site_a 2021-10-01 CO2  3.9622685      19      1
#> 20 site_b 2021-10-01 CO2  3.2499313      20      1
#> 21 site_a 2021-11-01 CO2  1.7419033      21      1
#> 22 site_b 2021-11-01 CO2  1.5511484      22      1
#> 23 site_a 2021-12-01 CO2  1.2133351      23      1
#> 24 site_b 2021-12-01 CO2  1.0829190      24      1
#> 25 site_a 2021-01-01 CO2  0.8347288       1      2
#> 26 site_b 2021-01-01 CO2  0.6741332       2      2
#> 27 site_a 2021-02-01 CO2  1.3672901       3      2
#> 28 site_b 2021-02-01 CO2  1.0613224       4      2
#> 29 site_a 2021-03-01 CO2  1.7443084       5      2
#> 30 site_b 2021-03-01 CO2  1.3906597       6      2
#> 31 site_a 2021-04-01 CO2  4.1451861       7      2
#> 32 site_b 2021-04-01 CO2  3.3212976       8      2
#> 33 site_a 2021-05-01 CO2  5.9120294       9      2
#> 34 site_b 2021-05-01 CO2  4.4778132      10      2
#> 35 site_a 2021-06-01 CO2  7.2487756      11      2
#> 36 site_b 2021-06-01 CO2  5.6816381      12      2
#> 37 site_a 2021-07-01 CO2 10.1179566      13      2
#> 38 site_b 2021-07-01 CO2  8.5211395      14      2
#> 39 site_a 2021-08-01 CO2  8.1122940      15      2
#> 40 site_b 2021-08-01 CO2  6.8952154      16      2
#> 41 site_a 2021-09-01 CO2  6.2227577      17      2
#> 42 site_b 2021-09-01 CO2  5.2422286      18      2
#> 43 site_a 2021-10-01 CO2  3.9622685      19      2
#> 44 site_b 2021-10-01 CO2  3.2499313      20      2
#> 45 site_a 2021-11-01 CO2  1.7419033      21      2
#> 46 site_b 2021-11-01 CO2  1.5511484      22      2
#> 47 site_a 2021-12-01 CO2  1.2133351      23      2
#> 48 site_b 2021-12-01 CO2  1.0829190      24      2