View source: R/colony-growth.R
bumbl | R Documentation |
Fits generalized linear models that assume bumblebee colonies will switch from growth to gyne production at some point, τ. This allows for a different switchpoint (τ) for each colony, chosen by maximum likelihood methods.
bumbl( data, t, formula, family = gaussian(link = "log"), colonyID = NULL, augment = FALSE, keep.model = FALSE, tau_optim_maxit = 100, ... )
data |
a dataframe or tibble with a column for colony ID (as a
|
t |
the unquoted column name of the time variable. |
formula |
a formula with the form |
family |
a description of the error distribution and link function.
This is passed to |
colonyID |
the unquoted column name of the colony ID variable. This is
required, so to run |
augment |
when FALSE, |
keep.model |
If TRUE, then the output will contain a list-column with
the models for each colony. This may be useful for extracting statistics
and performing model diagnostics not provided by |
tau_optim_maxit |
passed to |
... |
additional arguments passed to |
Colony growth is modeled as increasing exponentially until the
colony switches from producing workers to producing reproductive
individuals (drones and gynes), at which time the workers die and gynes
leave the colony, causing the colony to decline. The switch point,
τ, may vary among colonies. bumbl()
finds the value of
τ that maximizes likelihood and this "winning" model is used to
calculate statistics returned in the output. This function works by fitting
generalized linear models (GLMs) to modified colony growth data. Because of
this, the assumptions for GLMs apply, namely independence and homogeneity
of variance. See vignette("bumbl", package = "bumbl")
for more details on
the underlying math of the model.
A data.frame
with the additional class bumbldf
containing a
summary of the data with a row for every colony and the following columns:
converged
indicates whether the winning model converged.
tau
is the switchpoint, in the same units as t
, for
each colonyID
. The colony grows for τ weeks, then begins to
decline in week τ + 1.
logN0
is the intercept of the
growth function. It reflects actual initial colony size, if the colony
initially grows exponentially. It would also be lower if there were a few
weeks lag before growth started in the field.
logLam
is the
average (log-scale) colony growth rate (i.e., rate of weight gain per unit
t
) during the growth period.
decay
reflects the rate of decline during the decline period.
Equivalent to ln(δ) - ln(λ) (see vignette for more
in-depth explanation).
logNmax
is the maximum weight reached by each colony. It is a
function of tau
, logN0
and logLam
Additional columns are
coefficients for any covariates supplied in the formula
When augment = TRUE
, the original data are returned with these columns as
well as fitted values (.fitted
) residuals (.resid
) and standard error
(.se.fit
). When keep.model = TRUE
a list-column with the glm
models
for each colony is returned as well.
This function assumes there is a switchpoint and does not test whether the switchpoint model is significantly better than a log-linear model. As a result, it may estimate a switchpoint even if the data do not represent a true switchpoint. See the vignette for an example of how to extract the GLMs—you could compare them to a simpler log-linear model without the switchpoint by AIC or a likelihood ratio test to test the significance of the switchpoint.
Crone EE, Williams NM (2016) Bumble bee colony dynamics: quantifying the importance of land use and floral resources for colony growth and queen production. Ecology Letters 19:460–468. https://doi.org/10.1111/ele.12581
plot.bumbldf()
bumbl(bombus, colonyID = colony, t = week, formula = d.mass ~ week)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.