auto_rate automatically performs a rolling regression on a data frame to
determine the most linear, maximum, minimum, or interval rate of change
in oxygen concentration over time. First, a rolling regression of specified
width is performed on the entire dataset to obtain all possible values. The
computations are then ranked (or, arranged), based on the "
and the output is summarised.
data frame, or object of class
numeric. Width of the rolling regression. Defaults to
logical. Defaults to TRUE. Plot the results.
logical. Defaults to TRUE. Should parallel processing be used?
There are no units of measurement involved in
auto_rate. This is a
deliberate decision. Units are called in a later function when volumetric
and/or mass-specific rates of oxygen use are computed in
auto_rate() contains four ranking algorithms that can be called
with the argument
linear: Uses kernel density estimation (KDE) to detect the "most
linear" sections of the timeseries. This is achieved by using the smoothing
bandwidth of the KDE to re-sample the "peaks" in the KDE to determine linear
regions in the data.
max: regressions are arranged from highest values, to the lowest.
min: regressions are arranged from lowest values, to the highest.
interval: non-overlapping regressions are extracted from the rolled
regrssions. They are not ranked.
A list object of class
1 2 3 4 5 6 7 8 9 10 11
# most linear section of the entire data auto_rate(flowthrough.rd, parallel = FALSE) # LONG EXAMPLES ## Not run: # what is the lowest rate over a 10 minute (600s) period? auto_rate(sardine.rd, method = "min", width = 600, by = "time", parallel = FALSE) # what is the highest rate over a 10 minute (600s) period? auto_rate(sardine.rd, method = "max", width = 600, by = "time", parallel = FALSE) ## End(Not run)
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