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Inverse prediction from a modeler object

Source: R/13_inverse_predict.R
inverse_predict.modeler.Rd

Computes the x-value at which a fitted model reaches a user-specified response value (y-value).

Usage

# S3 method for class 'modeler'
inverse_predict(
  object,
  y,
  id = NULL,
  interval = NULL,
  tol = 1e-06,
  resolution = 1000,
  ...
)

Arguments

object

A fitted object of class modeler.

y

A numeric scalar giving the target y-value for which to compute the corresponding x.

id

Optional vector of uids for which to perform inverse prediction. If NULL, all groups are used.

interval

Optional numeric vector of length 2 specifying the interval in which to search for the root. If NULL, the interval is inferred from the range of the observed x-values.

tol

Numerical tolerance passed to uniroot for root-finding accuracy.

resolution

Integer. Number of grid points used to scan the interval.

...

Additional parameters for future functionality.

Value

A tibble with one row per group, containing:

  • uid – unique identifier of the group,

  • fn_name – the name of the fitted function,

  • lower and upper – the search interval used,

  • y – the predicted y-value (from the function at the root),

  • x – the x-value at which the function reaches y.

Details

The function uses numeric root-finding to solve f(t, ...params) = y. If no root is found in the interval, NA is returned.

See also

predict.modeler, uniroot

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_lin_plat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = c(15, 2, 45)
  )
print(mod_1)
#> 
#> Call:
#> Canopy ~ fn_lin_plat(DAP, t1, t2, k) 
#> 
#> Residuals (`Standardized`):
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#> -1.7789  0.0000  0.0000  0.1350  0.1334  2.2361 
#> 
#> Optimization Results `head()`:
#>  uid   t1   t2     k    sse
#>    2 35.1 61.1 100.0 5.7008
#>   15 38.4 70.1  99.7 0.9157
#>   45 38.3 64.7 100.0 0.0026
#> 
#> Metrics:
#>  Groups      Timing Convergence  Iterations
#>       3 0.6103 secs        100% 407.67 (id)
#> 
inverse_predict(mod_1, y = 50)
#> # A tibble: 3 × 6
#>     uid fn_name     lower upper     y     x
#>   <dbl> <chr>       <dbl> <dbl> <dbl> <dbl>
#> 1     2 fn_lin_plat     0   100    50  48.1
#> 2    15 fn_lin_plat     0   100    50  54.3
#> 3    45 fn_lin_plat     0   100    50  51.5
inverse_predict(mod_1, y = 75, interval = c(20, 80))
#> # A tibble: 3 × 6
#>     uid fn_name     lower upper     y     x
#>   <dbl> <chr>       <dbl> <dbl> <dbl> <dbl>
#> 1     2 fn_lin_plat    20    80    75  54.6
#> 2    15 fn_lin_plat    20    80    75  62.3
#> 3    45 fn_lin_plat    20    80    75  58.1