uncertainty.datalogger {embryogrowth} | R Documentation |
Uncertainty of average temperatures obtained using temperature data logger
Description
Calculate the uncertainty of average temperature dependent on the
characteristics of a data logger and sampling rate.
The temperature is supposed to be uniformaly distributed with min and max
being -accuracy and +accuracy.
Usage
uncertainty.datalogger(
max.time = 0,
sample.rate = 0,
accuracy = 0.5,
resolution = 1,
replicates = 10000,
method = function(x) {
2 * qnorm(0.975) * sd(x)
}
)
Arguments
max.time |
being the maximum time to record in minutes |
sample.rate |
The sample rates in minutes |
accuracy |
The accuracy of the data logger in °C |
resolution |
The resolution of the data logger in °C |
replicates |
The number of replicates to estimate uncertainty. |
method |
The fonction that will be used to return the uncertainty. |
Details
uncertainty.datalogger Calculate the uncertainty of the average temperature calculated using data gathered by a data logger.
Value
The function will return the uncertainty of the average temperature for the considered period as being the 95% range where the true average temperature should be.
Author(s)
Marc Girondot
References
Girondot M, Godfrey MH, Guillon J, Sifuentes-Romero I (2018).
“Understanding and integrating resolution, accuracy and sampling rates of temperature data loggers used in biological and ecological studies.”
Engineering Technology Open Access Journal, 2(4), 55591.
See Also
Other Data loggers utilities:
calibrate.datalogger()
,
movement()
Examples
## Not run:
library(embryogrowth)
# Exemple using the hypothesis of Gaussian distribution
uncertainty.datalogger(sample.rate=30, accuracy=1, resolution=0.5,
method=function(x) {2*qnorm(0.975)*sd(x)})
# Example without hypothesis about distribution, using quantiles
uncertainty.datalogger(sample.rate=30, accuracy=1, resolution=0.5,
method=function(x) {quantile(x, probs=c(0.975))-
quantile(x, probs=c(0.025))})
par(mar=c(4, 4, 1, 1))
plot(x=10:120, uncertainty.datalogger(sample.rate=10:120,
accuracy=0.5,
resolution=1),
las=1, bty="n", type="l",
xlab="Sample rate in minutes",
ylab=expression("Uncertainty in "*degree*"C"),
ylim=c(0, 0.15), xlim=c(0, 120))
lines(x=10:120, uncertainty.datalogger(sample.rate=10:120,
accuracy=1,
resolution=0.5), col="red")
lines(x=10:120, uncertainty.datalogger(sample.rate=10:120,
accuracy=1,
resolution=1), col="blue")
lines(x=10:120, uncertainty.datalogger(sample.rate=10:120,
accuracy=0.5,
resolution=0.5), col="yellow")
legend("topleft", legend=c("Accuracy=0.5, resolution=0.5",
"Accuracy=0.5, resolution=1",
"Accuracy=1, resolution=0.5",
"Accuracy=1, resolution=1"), lty=1,
col=c("yellow", "black", "red", "blue"),
cex=0.6)
## End(Not run)