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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(L = 100, k = 4, t0 = 40),
    subset = plots
  )

Plotting predictions and derivatives

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

plot derivatives

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

plot coef

# 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)

plot derivatives

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.