analyze_sensitivity(
  data,
  funct,
  test1 = NA,
  values1,
  test2 = NA,
  values2,
  element_out = 1,
  ...
)

# Read in your data
# Note that this data is coming from data supplied by the package
# hence the complicated argument in read.csv()
# This dataset is a CO2 by light response curve for a single sunflower
data <- read.csv(system.file("extdata", "A_Ci_Q_data_1.csv",
  package = "photosynthesis"
))

# Define a grouping factor based on light intensity to split the ACi
# curves
data$Q_2 <- as.factor((round(data$Qin, digits = 0)))

# Convert leaf temperature to K
data$T_leaf <- data$Tleaf + 273.15

# Run a sensitivity analysis on gamma_star and mesophyll conductance
# at 25 Celsius for one individual curve
pars <- analyze_sensitivity(
  data = data[data$Q_2 == 1500, ],
  funct = fit_aci_response,
  varnames = list(
    A_net = "A",
    T_leaf = "T_leaf",
    C_i = "Ci",
    PPFD = "Qin"
  ),
  useg_mct = TRUE,
  test1 = "gamma_star25",
  element_out = 1,
  test2 = "g_mc25",
  fitTPU = TRUE,
  Ea_gamma_star = 0,
  Ea_g_mc = 0,
  values1 = seq(
    from = 20,
    to = 40,
    by = 2
  ),
  values2 = seq(
    from = 0.5,
    to = 2,
    by = 0.1
  )
)

# Graph V_cmax
ggplot(pars, aes(x = gamma_star25, y = g_mc25, z = V_cmax)) +
  geom_tile(aes(fill = V_cmax)) +
  labs(
    x = expression(Gamma * "*"[25] ~ "(" * mu * mol ~ mol^
      {
        -1
      } * ")"),
    y = expression(g[m][25] ~ "(" * mu * mol ~ m^{
      -2
    } ~ s^{
      -1
    } ~ Pa^
      {
        -1
      } * ")")
  ) +
  scale_fill_distiller(palette = "Greys") +
  geom_contour(colour = "Black", size = 1) +
  theme_bw()