CalibrationSet {cvasi} | R Documentation |
Calibration set
Description
A calibration set combines a scenario, observed data, and an
optional weighting factor into one object. The calibration set is used to fit
model parameters to observed data using calibrate()
.
Usage
caliset(scenario, data, weight = 1.0, tag = NULL)
Arguments
scenario |
a scenario describing conditions during the experiment |
data |
a |
weight |
optional |
tag |
optional value to identify the data, e.g. a study number |
Details
A calibration set usually represents a single experiment or trial.
Multiple experimental replicates can be combined into a single set, if model
parameters are identical between trials.
If model parameters were modified during a trial, e.g. a pump failure occurred
or flow rates changed, this can be represented by using a scenario sequence
instead of a basic scenario. Please refer to sequence()
for details.
Weighting
An optional weighting factor can be used to scale the error term of a
whole set or of individual data points when fitting parameters using e.g.
calibrate()
.
The vector of weights must either be of length one or have the same length as the dataset. In the former case, the same weight will be applied to all values in the dataset. In the latter, individual weights are applied for each data point.
Value
caliset()
returns a calibration set object
Examples
library(dplyr)
# Get observed biomass during control experiment by Schmitt et al. (2013)
observed <- Schmitt2013 %>%
filter(ID == "T0") %>%
select(t, BM=obs)
# Create a scenario that represents conditions during experiment
scenario <- metsulfuron %>%
set_param(c(k_phot_fix=TRUE, k_resp=0, Emax=1)) %>%
set_init(c(BM=12)) %>%
set_noexposure()
# Create a calibration set
cs <- caliset(scenario, observed)
# Fit parameter 'k_phot_max' to observed biomass growth from experiment
calibrate(
cs,
par=c(k_phot_max=1),
output="BM",
method="Brent", # Brent is recommended for one-dimensional optimization
lower=0, # lower parameter boundary
upper=0.5 # upper parameter boundary
) -> fit
fit$par