Description Usage Arguments Details Note References Examples
weib.limit
estimates either the the lower or upper limit of
a distribution of numbers given it. This is used in the manuscript
to calculate the onset of flowering given a set of flowering dates.
1 2 3 4 | weib.limit(x, k = NULL, upper = FALSE, alpha = 0.05)
weib.limit.bootstrap(x, k = NULL, n = 1000, max.iter = 10,
upper = FALSE)
|
x |
vector of dates/times of observations, given as numbers |
k |
how many entries in 'x' will be used to calculate the estimate. See 'Methods' in Pearse et al. (2017) for an explanation of why not all numbers need be used in the estimate |
upper |
whether to calculate the upper limit (if TRUE) or the lower limit (if FALSE, the default). In the context of plant flowering phenology, the default option calculates when flowering started (the focus of this manuscript) |
alpha |
the alpha value for the confidence intervals for the estimate of the limit of the distribution |
n |
how many times to run the bootstrapping |
max.iter |
it is not always possible to calculate an estimate for the limit (see Methods), and this means it is sometimes not possible to calculate an estimate across all bootstraps. If such a case occurs, this parameter sets how many times the function will try the bootstrapping again until the problem doesn't occur. |
weib.se.bootstrap
bootstraps an estimate of the Standard
Error for a particular estimate of the limit of a distribution. As
discussed in the Methods section of the manuscript, care should be
taken when bootstrapping estimates: the confidence intervals of
these estimates are asymmetrical, and so a single SE value should
be interpreted with caution. There are many ways of calculating
bootstrapped values, and (personally) I would advise you to write
your own function to make sure you're comfortable with what's going
on.
Smith (1987) discusses how there is a trade-off between
choosing a value of k
that is sufficiently large so as
to detect signal, but not so large as to introduce signal from
the bulk (centre) of the distribution. When there is evidence
that the confidence intervals are being influenced by the bulk
of the distribution this function returns NA
confidence
intervals and issues a warning. The estimate of the limit
itself seems unaffected, but as with any statistical method you
should inspect your estimates to ensure they make sense. This
is a rare occurrence, WDP should add!
Pearse, W. D., Davis, C. C., Inouye, D. W., Primack, R. B., & Davies, T. J. (2017). A statistical estimator for determining the limits of contemporary and historic phenology. Nature Ecology & Evolution, 1. DOI: 10.1038/s41559-017-0350-0 Smith, R. L. (1987). Estimating tails of probability distributions. The annals of Statistics, 1174-1207.
1 2 3 4 5 6 7 8 9 10 11 12 | # Gather some observations of when flowers were in bloom
observations <- 5:15
# Estimate the onset of flowering
weib.limit(observations)
# Estimate the end of flowering
weib.limit(observations, upper=TRUE)
# Change the alpha value for the confidence about those observations
weib.limit(observations, alpha=.2)
# Make use of fewer observations in estimating the onset (note the CI widen as a result)
weib.limit(observations, k=10)
# Bootstrap some confidence limits about the estimate
weib.limit.bootstrap(observations)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.