---
title: "Sensitivity analysis"
author: "Joseph R. Stinziano and Christopher D. Muir"
date: "`r Sys.Date()`"
output: rmarkdown::html_document
vignette: >
 %\VignetteIndexEntry{sensitivity-analysis}
 %\VignetteEngine{knitr::rmarkdown}
 %\VignetteEncoding{UTF-8}
---

This vignette outlines a set of functions that can be used to assess the
sensitivity of data outputs to assumed parameters. For example, assuming
different values of GammaStar, mesophyll conductance, and light absorbance
on fitted gas exchange parameters.

```{r, echo = TRUE, eval = FALSE}

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

#Run a sensitivity analysis on GammaStar 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 = 4),
                                   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 = 40,
                            test2_ref = 1)
#Plot sensitivity
ggplot(par2, aes(y = CE_gamma_star25, x = CE_g_mc25, colour = V_cmax))+
  labs(x = expression(g_mc[25]~"Control Coefficient"),
       y = expression(Gamma[25]~"Control Coefficient")) +
  geom_point() + 
  theme_bw() 

ggplot(par2, aes(y = CE_gamma_star25, x = V_cmax, colour = gamma_star25))+
  geom_point() + 
  theme_bw() 
#Note that in this case a missing point appears due to an infinity
#Can also plot sensitivity measures in 2 dimensions
ggplot(par2, aes(x = gamma_star25, y = g_mc25, z = CE_gamma_star25))+
geom_tile(aes(fill = CE_gamma_star25)) +
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()

plot(PE_gamma_star25 ~ gamma_star25, par2)
```