Fitting many functions across groups — fit

We are no longer updating this function. Please use generic methods like map instead. See vignette("light-response") for an example.

Usage

fit_many(data, funct, group, progress = TRUE, ...)

Arguments

data: Dataframe
funct: Function to fit
group: Grouping variables
progress: Flag. Show progress bar?
...: Arguments for the function to fit. Use ?functionname to read the help file on available arguments for a given function.

Value

fit_many fits a function across every instance of a grouping variable.

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

# Fit many curves
fits = fit_many(
  data = data,
  varnames = list(
    A_net = "A",
    T_leaf = "T_leaf",
    C_i = "Ci",
    PPFD = "Qin"
  ),
  funct = fit_aci_response,
  group = "Q_2"
)
#> 
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# Print the parameters
# First set of double parentheses selects an individual group value
# Second set selects an element of the sublist
fits[[3]][[1]]
#>   Num  V_cmax V_cmax_se    J_max        J       J_se V_TPU V_TPU_se        R_d
#> 6   0 8.94862 0.5509706 47.01527 16.63315 0.08692268  1000       NA -0.1565895
#>      R_d_se      cost citransition1 citransition2 V_cmax_pts J_max_pts
#> 6 0.1264438 0.1194886      441.2967      1442.493          8         4
#>   V_TPU_pts alpha alpha_g gamma_star25 Ea_gamma_star K_M25   Ea_K_M  g_mc25
#> 6         0  0.24       0        42.75         37830 718.4 65508.28 0.08701
#>   Ea_g_mc Oconc theta_J
#> 6       0    21    0.85

# Print the graph
fits[[3]][[2]]
#> Warning: Removed 12 rows containing missing values or values outside the scale range
#> (`geom_line()`).


# Compile graphs into a list for plotting
fits_graphs = compile_data(fits,
  list_element = 2
)


# Compile parameters into dataframe for analysis
fits_pars = compile_data(fits,
  output_type = "dataframe",
  list_element = 1
)
# }