photosynthesis (version 2.0.3)

compute_sensitivity: Computing measures of sensitivity

Description

Computing measures of sensitivity

Usage

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

Value

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.

Arguments

data

Dataframe with output from sensitivity_analysis()

varnames

Variable names

test1_ref

Reference value for parameter

test2_ref

Reference value for parameter

References

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

Examples

# \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
# }