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