calculate the below five objective functions that are commonly used to measure goodness of fit: 1) Nash-Sutcliffe Efficiency coefficient (NSE), 2) Root Mean Square Error (RMSE), 3) Mean Absolute Error (MAE), 4) Relative Absolute Error (RAE), and 5) Pearson's r (Pearson).
objective_fun(
sim,
obs,
fun = c("NSE", "RMSE"),
start.date,
end.date,
min.depth,
max.depth,
by.value
)a list of objective function values.
a matrix of a simulated water quality variable values with column of time and row of depth. This matrix can be generated by running the "interpol" function.
a data frame having three columns to describe observed values of a water quality variable. These three columns are 'Date' (as '%Y-%m-%d'), 'Depth', and the designated variable name which can be found from the var.name column of 'data(output_name)'. An example of such a data frame can be found with 'data(obs_temp)'
objective function(s) to be calculated. Select any from 'NSE', 'RMSE', 'MAE', 'RAE', and 'Pearson'. Multiple selections are allowed.
the start and end simulation dates for the DYRESM-CAEDYM model run. The date format must be "%Y-%m-%d".
the minimum and maximum depths of the simulation matrix.
the value of increment at which the depth of layers increases from the mim.depth to max.depth in the simulation matrix.