Compare performance of different models

computes indices of model performance for different models at once and hence allows comparison of indices across models.

Usage

performance(..., metrics = "all", metadata = FALSE, digits = 2)

Arguments

...

Multiple model objects (only of class `modeler`).

metrics

Can be "all" or a character vector of metrics to be computed (one or more of "logLik", "AIC", "AICc", "BIC", "Sigma", "SSE", "MAE", "MSE", "RMSE", "R2"). "all" by default.

metadata

Logical. If TRUE, metadata is included with the performance metrics. Default is FALSE.

digits

An integer. The number of decimal places to round the output. Default is 2.

Value

A data.frame with performance metrics for models in (...).

Examples

library(flexFitR)
data(dt_potato)
# Model 1
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 90),
    subset = 40
  )
print(mod_1)
#> 
#> Call:
#> Canopy ~ fn_linear_sat(DAP, t1, t2, k) 
#> 
#> Residuals (`Standardized`):
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#> -1.7789  0.0000  0.0000  0.0000  0.1334  1.2452 
#> 
#> Optimization Results `head()`:
#>  uid   t1   t2   k    sse
#>   40 34.8 60.6 100 0.0545
#> 
#> Metrics:
#>  Groups      Timing Convergence Iterations
#>       1 1.0504 secs        100%   527 (id)
#> 
# Model 2
mod_2 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_logistic",
    parameters = c(L = 100, k = 4, t0 = 50),
    subset = 40
  )
print(mod_2)
#> 
#> Call:
#> Canopy ~ fn_logistic(DAP, L, k, t0) 
#> 
#> Residuals (`Standardized`):
#>     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
#> -1.56658 -0.63135  0.03151 -0.18197  0.10167  1.20076 
#> 
#> Optimization Results `head()`:
#>  uid    L     k   t0  sse
#>   40 99.8 0.199 47.7 37.4
#> 
#> Metrics:
#>  Groups      Timing Convergence Iterations
#>       1 1.2006 secs        100%   869 (id)
#> 
# Model 3
mod_3 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_lin",
    parameters = c(m = 20, b = 2),
    subset = 40
  )
print(mod_3)
#> 
#> Call:
#> Canopy ~ fn_lin(DAP, m, b) 
#> 
#> Residuals (`Standardized`):
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#> -1.1490 -0.6400 -0.2358  0.0000  0.8683  1.3178 
#> 
#> Optimization Results `head()`:
#>  uid    m     b  sse
#>   40 1.29 -17.8 2644
#> 
#> Metrics:
#>  Groups      Timing Convergence Iterations
#>       1 0.9738 secs        100%   446 (id)
#> 
performance(mod_1, mod_2, mod_3, metrics = c("AIC", "AICc", "BIC", "Sigma"))
#>           fn_name uid df nobs p   AIC  AICc   BIC Sigma
#> 1        fn_lin_3  40  3    8 2 75.11 81.11 75.35 20.99
#> 2 fn_linear_sat_1  40  4    8 3 -9.21  4.12 -8.89  0.10
#> 3   fn_logistic_2  40  4    8 3 43.04 56.37 43.35  2.73