climwin v1.2.3 includes mostly minor internal changes to make
climwin compatible with upcoming R v4.0.0
None
None
plyr, which was an artefact of old
code.climwin v1.2.2 includes a number of important bug fixes that may affect ongoing analyses.
A major bug has been detected when running slidingwin()
with cinterval = "week" and type = "absolute".
In this particular case, results were nonsensical and would not match
similar results generated using cinterval = "day"/"month".
This issue was caused by the way we calculated the number of weeks in a
year. This bug has now been fixed. Any previous analyses using this
approach should be reconsidered.
When using cinterval = "month", it is possible to
identify one best window with a large model weight (>0.95). This led
to errors in plotwin(), which expects multiple models with
small model weights. This issue has now been fixed, and
plotwin() can work with results where there is a single
best window.
slidingwin() allows users to provide weights in the
baseline model. However, when these weights are uniform at 1, this led
to an error in the way slidingwin() updates the baseline
model during the window fitting process. The method for dealing with
weights has been changed and this bug has been removed.
climwin v1.2.1 is now fully compatible with R v3.6.0. To report
errors and bugs or ask questions please visit the climwin
google
group.
R v3.6.0 contains a new default method for the function
sample(), which is used inside the slidingwin
function in climwin. These changes will affect the reproducibility of
previous results run on older R version. Old results can be reproduced
by running RNGversion(vstr = "3.5.3"), which will revert
back to the old method for conducting sample().
No major changes
k-fold cross validation can now be conducted with k = 1.
With v3.0 of ggplot2 some slight compatibility changes were introduced when creating plots. These have now been fixed.
Previous versions of climwin used the
print() function to write text in the console. This is now
done using message() and warning(), which
allows users to suppress information if desired (e.g. with
suppressMessages()).
Our newest version includes a number of important tweaks and bug
fixes, as well as an updated help vignette. To report errors and bugs or
ask questions please visit the climwin google
group.
Older versions of climwin allowed the use of mixed
effects models with the package lme4; however, there have
been a number of requests for nlme compatability as an
alternative. This has been made possible in v1.2.0, with the option to
include constant and exponential variance functions for model weighting
(varIdent and varExp respectively). Note, however,
that k-fold cross validation is currently not available with
nlme models.
N.B. Please see minor changes below for other updates relating to mixed effects models.
weightwinAlong with the use of randwin, k-fold cross validation
is an important tool for overcoming potential
issues of over-fitting in climwin. K-fold cross
validation was previously only functional in slidingwin,
but has now been included with weightwin, using the
argument k.
Users may run climwin with cinterval == “week”/“month”
in cases where only weekly/monthly data is available but also to reduce
computational time when using daily data (see news for v1.1.0). This
raises potential concerns when using climatic thresholds (e.g. arguments
upper, lower, binary). When daily data is available, users may wish to
apply thresholds before data is grouped by week/month (e.g. for
each week, what is the mean number of days that temperature > x).
Alternatively, users may wish to apply thresholds after week/month
grouping has occurred (e.g. is mean weekly temperature > x). v1.2.0
now includes a prompt allowing users to select between these two
possibilities.
weightwinWhile running weightwin returns a number of plots to
help the user track the optimisation process. We have replaced the
bottom left plot to show the plotted relationship between the weighted
mean of climate and the response variable specified in the baseline
model. This allows users to spot any outliers that may be driving
patterns in the data.
N.B. This plot does not account for any additional variables included in the baseline model. It should be used as a tool for identifying outliers rather than an accurate reflection of the statistical relationship between the variables.
When using mixed effects models in climwin it is
necessary to use a maximum likelihood [ML] approach rather than
restricted maximum likelihood [REML]. In v1.2.0 this has now been made
mandatory. Any models fitted with REML will be refitted with ML (along
with an accompanying message).
N.B. This means that the best model output will also be fitted with ML. Users can manually refit the best model with REML if desired.
The method use to group data at a weekly scale has been changed
slightly. This may lead to slight changes in results when using the
argument cinterval = "week" compared to previous versions;
however, changes should be minimal and the overall interpretation of
results should remain the same.
Previously, climwin printed any messages and warnings
immediately during model fitting; however, this disrupted the progress
bar making it difficult for users to follow the progress of their
analysis. All warning messages are now displayed at the end of the
process. Messages associated with model fitting (such as rank-deficiency
when using lmer) are now suppressed. These messages are
expected when fitting very small climate windows and so should not be a
concern for users.
Fixed a bug where the ‘slope’ statistic was calculated with the wrong sign.
Our newest version adds a number of useful features to
climwin as well as a few bug fixes. In addition, we have
now created a climwin google
group for users to ask questions about the package. Please report
any errors or bugs on this forum.
In older versions of climwin, climate data was extracted
from the fitted climate window and included as a fixed effect in the
specified baseline model. This precluded the use of more complex model
structures, such as interactions between climate and other fixed effects
or mixed effects models with random climate slopes. We now include a
more versatile method for specifying model structure in
climwin. This involves the inclusion of a dummy ‘climate’
variable in the baseline model (N.B. the variable must
be called ‘climate’). This dummy variable will then be replaced with
climate data from each fitted climate window, maintaining the original
model structure. See an example of baseline model structure below:
baseline = lm(response ~ climate*sex)
Using this more versatile model construction makes the argument
func redundant (i.e. the argument used to specify
quadratic, cubic etc. terms). Users can now structure their model
manually to test for these effects.
Note: We have maintained functionality for the original baseline model design. If no variable called ‘climate’ is provided the original method will be used.
In previous versions, the argument cmissing could be
designated as either TRUE or FALSE. When FALSE, the presence of missing
values in any tested climate window would return an error. When TRUE,
all climate windows containing missing values would be removed from our
analysis. On further consideration, we felt it was unwise to remove data
from our analysis. As an alternative, we now provide two methods to
estimate the value of NA records. “method1” will replace the value of a
missing cell with the mean of the two preceding and following records.
“method2” will replace the value of a missing cell with the mean of all
other records on the same date. For more detail on dealing with missing
data please read our FAQ.
As weightwin uses an optimisation function, there is the possibility
that the function may fail to converge or will converge on a local
optima. Because of these issues, a single run of weightwin may not
provide an accurate measure of the climate landscape. Users should
instead run multiple iterations of weightwin with varying starting
parameters to help find the global optima. The argument n
in weightwin allows users to specify the number of iterations to run,
with starting parameters randomly assigned in each run. weightwin will
then return a summary table of results from all iterations.
The original design of climwin required users to provide
their climate data at a daily resolution, even if users had only a
single recorded value across each month/week. This caused some confusion
with users. climwin can now deal with climate data at a
monthly or weekly resolution (i.e. one record for each month/week).
Running climate window analysis at a monthly or weekly scale also
provides a method for dealing with missing climate
data.
climwin version 1.0.0 includes a number of major changes
to coincide with the release of our corresponding paper [1].
We have tried to make most of our changes backwards compatible, but any
major issues should be reported to the package maintainer
(liam.bailey@anu.edu.au).
climwin aims to distinguish between two separate methods
for testing climate windows. The commonly used sliding window approach
[1]
and less common weighted window approach [2].
To ensure the distinction between these two methods is clear, the
function climatewin has been made redundant and been replaced
with the function slidingwin. Parameters used in
slidingwin and climatewin are identical. Users can now
conduct a sliding window analysis using slidingwin and a
weighted window analysis using weightwin.
The function randwin can now also be used to conduct randomisations with a weighted window approach (i.e. using the function weightwin). Users must now define whether randomisations are to be conducted using a sliding window (“sliding”) or weighted window (“weighted”) approach with the parameter ‘window’. Note that all parameters from weightwin will be required to run randwin using a weighted window approach (e.g. ‘par’, ‘weightfunc’).
When a group of biological measurements covers two years (e.g. Southern hemisphere species which breed between November - February [2]) use of ‘absolute’ climate windows will cause these measurements to be split across different calendar years. To overcome this issue, we include a ‘cohort’ parameter to our functions.
The cohort variable will determine which biological measurements should be grouped together (e.g. measurements from the same breeding season), and ensure that these measurements share the same reference day. The cohort variable should come from the same dataset as the ‘bdate’ parameter (i.e. variables should have equal lengths).
See our advanced vignette for more details:
vignette("advanced_climwin", package = "climwin")
Climate window analysis often requires large amounts of data to effectively determine periods of climate sensitivity. Often this is achieved through temporal replication (collecting many years of data on the same population) but can also be achieved through spatial replication (collecting data on multiple populations), or, ideally, a combination of the two. The new parameter ‘spatial’ allows users to carry out a climate window analysis with data from multiple populations by linking each set of biological measurements to a corresponding set of climate data.
Users can include data from multiple study sites/populations in their climate dataset (i.e. the dataset used for parameters ‘cdate’ and ‘xvar’), with a new site ID variable included to distinguish between different sites. Similarly, the user can add a new site ID variable to the biological dataset that can be used to link biological measurements to the corresponding climate data. When carrying out analyses the user can then include the parameter ‘spatial’, a list item, containing the biological site ID variable and climate site ID variable respectively. During model fitting, the climate window analysis will extract different climate data for each biological record based on the provided site ID.
N.B. Spatial replication in climate window analysis works on the assumption that all populations share the same period of climate sensitivity. If this is NOT the case, populations should be analysed separately.
See our advanced vignette for more details:
vignette("advanced_climwin", package = "climwin")
Proportional hazard models may often be useful for climate window analyses on phenological data. We have included the ability for users to fit proportional hazard models for the parameter ‘baseline’ using the function coxph. For more detail on understanding the use of proportional hazard models for phenology analysis see [2].
In previous versions of climwin climate windows have
been compared visually using a number of methods (e.g. deltaAICc
distribution, model weights). In this newest version, we have included
two metrics that allow for a standard method of distinguishing real
periods of climate sensitivity in biological data.
These two metrics, \(P_{C}\) and \(P_{\Delta AICc}\), determine the likelihood that a given climate window would occur by chance, given the results of a randwin analysis on the same data. They can be calculated using the new function pvalue. For more information on the effectiveness of the new metrics, please see [1].
Although climwin helps us move away from the selection
of arbitrary climate windows the method is inherently exploratory,
raising concerns about overfitting. Climate data from short duration
time windows are particularly likely to show spurious relationships in
climate window analysis [1].
The inclusion of the function pvalue (above) reduces the chance
that these short duration windows will be mistaken as ‘real’ climate
signals; however, these spurious windows can still cause problems when
conducting multi-model inferencing as the short duration windows may be
distinctly different from other top models. As a solution, we include
the parameter ‘exclude’ in the functions slidingwin and
randwin. This allows users to exclude windows of a specific
duration and lag to prevent these small windows from interfering with
analyses.
Parameter changes:
‘furthest’ and ‘closest’ are now combined into a single parameter ‘range’.
‘cutoff.day’ and ‘cutoff.month’ are now combined into a single parameter ‘refday’.
‘cvk’ parameter is now redundant, replaced with parameter ‘k’.
‘thresh’ parameter is now redundant, replaced with parameter ‘binary’.
‘type’ now accepts possible arguments ‘absolute’ and ‘relative’ (rather than ‘fixed’ and ‘variable’).
Fixed bug which caused convergence issues using cross-validation.
Fixed serious bug causing an error in ‘plotwin’ and ‘plotall’. Naming mismatch in the ‘closest’ column in climatewin$Dataset. Column name changes from Closest to closest.
Our newest release aims to speed up the functions and provide greater
versatility to users. In addition, we have produced a vignette providing
a detailed introduction on how to use the climwin package. See
vignette("climwin") for more.
We have made some changes to the names and levels or parameters that should be checked in the help documentation.
Changes to parameter names and levels (e.g. Cinterval levels are now in the format “day”, “week”, “month” rather than “D”, “W”, “M”). Please check the help documentation of each function to familiarise yourself with the new function wording.
Function climatewin (and corresponding functions)
now contains parameter CVK. This provides the ability to run k-fold
cross validation during climate window analysis. This provides a further
check against finding strong climate signals by chance.
Function climatewin (and corresponding functions)
now contains parameters upper, lower and thresh. These allow users to
adapt their climate data to consider climatic thresholds. This will be
useful for those interested in investigating topics like growing degree
or chill days.
Function climatewin (and corresponding functions)
now contains parameter centre. This allows users to carry out
within-group centring with their climate data. Note that within-group
centreing will only test linear relationships.
Function climatewin now includes functionality to
test multiple parameter combinations (e.g. func = c(“lin”, “quad”)) in
succession. Please see the vignette vignette("climwin") for
more detail.
Function crosswin now includes parameter stat2. This
allows users to test the correlation between two climate variables using
different aggregate statistics (e.g. Mean temperature and minimum
rainfall).