# Plotting options

## Loading dataset and libraries

    library(flexFitR)
    library(dplyr)
    library(kableExtra)
    library(ggpubr)
    library(purrr)
    data(dt_potato)
    head(dt_potato) |> kable()

Trial | Plot | Row | Range | gid | DAP | Canopy | GLI
--- | --- | --- | --- | --- | --- | --- | ---
HARS20_chips | 1 | 1 | 1 | W17037-24 | 0 | 0.000 | 0.0000000
HARS20_chips | 1 | 1 | 1 | W17037-24 | 29 | 0.000 | 0.0027216
HARS20_chips | 1 | 1 | 1 | W17037-24 | 36 | 0.670 | -0.0008966
HARS20_chips | 1 | 1 | 1 | W17037-24 | 42 | 15.114 | 0.0322547
HARS20_chips | 1 | 1 | 1 | W17037-24 | 56 | 75.424 | 0.2326896
HARS20_chips | 1 | 1 | 1 | W17037-24 | 76 | 99.811 | 0.3345619

## Modeling

    plots <- 2:7
    mod <- dt_potato |>
      modeler(
        x = DAP,
        y = Canopy,
        grp = Plot,
        fn = "fn_logistic",
        parameters = c(a = 4, t0 = 40, k = 100),
        subset = plots
      )

## Plotting predictions and derivatives

    # Raw data with fitted curves
    plot(mod, type = 1, color = "blue", id = plots, title = "Fitted curves")

    # Model coefficients
    plot(mod, type = 2, color = "blue", id = plots, label_size = 10)

    # Fitted curves only
    c <- plot(mod, type = 3, color = "blue", id = plots, title = "Fitted curves")

    # Fitted curves with confidence intervals
    d <- plot(mod, type = 4, n_points = 200, title = "Fitted curve (uid = 2)")

    # First derivative with confidence intervals
    e <- plot(mod, type = 5, n_points = 200, title = "1st Derivative (uid = 2)")

    # Second derivative with confidence intervals
    f <- plot(mod, type = 6, n_points = 200, title = "2nd Derivative (uid = 2)")
    ggarrange(c, d, e, f)