compute_sensitivity: Computing measures of sensitivity

In photosynthesis: Tools for Plant Ecophysiology & Modeling

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
)

Arguments

data	Dataframe with output from sensitivity_analysis()
varnames	Variable names
test1_ref	Reference value for parameter
test2_ref	Reference value for parameter

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.

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

# 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

photosynthesis documentation built on Aug. 15, 2023, 9:08 a.m.