Computing measures of sensitivity

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

- data
Dataframe with output from sensitivity_analysis()

- varnames
Variable names

- test1_ref
Reference value for parameter

- test2_ref
Reference value for parameter

compute_sensitivity calculates two sets of sensitivity measures: parameter effect (Bauerle et al., 2014), and control coefficient (Capaldo & Pandis, 1997). This function is useful in determining how much a given input (assumed or otherwise) can affect the model output and conclusions. Particularly useful if a given parameter is unknown during a fitting or modeling process.

Bauerle WL, Daniels AB, Barnard DM. 2014. Carbon and water flux responses to physiology by environment interactions: a sensitivity analysis of variation in climate on photosynthetic and stomatal parameters. Climate Dynamics 42: 2539-2554.

Capaldo KP, Pandis SN 1997. Dimethylsulfide chemistry in the remote marine atmosphere: evaluation and sensitivity analysis of available mechanisms. J Geophys Res 102:23251-23267

```
# \donttest{
# 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
# }
```