compute_sensitivity(
  data,
  varnames = list(Par = "Par", test1 = "test1", test2 = "test2"),
  test1_ref,
  test2_ref
)

# 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 = 60,
    by = 2
  ),
  values2 = seq(
    from = 0.2,
    to = 2,
    by = 0.1
  )
)
# Compute measures of sensitivity
par2 <- compute_sensitivity(
  data = pars,
  varnames = list(
    Par = "V_cmax",
    test1 = "gamma_star25",
    test2 = "g_mc25"
  ),
  test1_ref = 42,
  test2_ref = 1
)
# Plot control coefficients
ggplot(par2, aes(y = CE_gamma_star25, x = CE_g_mc25, colour = V_cmax)) +
  geom_point() +
  theme_bw()
# Note that in this case a missing point appears due to an infinity