knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width=7, fig.height=5 )
This vignette details the structure and construction of the incidence_fit
and
incidence_fit_list
classes, which are produced by the fit()
and
fit_optim_split()
functions, respectively. By the end of this tutorial, you
should be able to construct incidence_fit
and incidence_fit_list
objects for
use with your own models.
incidence_fit
objectAn incidence_fit
object contains three elements:
$model
: The model fit to an incidence
object. Currently, this represents a
log-linear model, but it can be any model. $info
: Information derived from the modelr
The growth rater.conf
the confidence interval of r
pred
a data frame containing the predictions of the model using the true
dates (dates
), their numeric version used in the model (dates.x
), the
predicted value (fit
), and the lower (lwr
) and upper (upr
) bounds of
the associated confidence interval.doubling
the predicted doubling time in days (only if r
is positive)doubling.conf
the confidence interval of the doubling timehalving
the predicted halving time in days (only if r
is negative)halving.conf
the confidence interval of the halving time$origin
: the date corresponding to day '0'Internally, when fit()
is run, these elements are constructed by
function incidence:::extract_info()
. First we need to setup data. We will use
simulated Ebola outbreak data from the outbreaks package over weekly intervals
and calculate the fit for the first 20 weeks:
library(outbreaks) library(incidence) dat <- ebola_sim$linelist$date_of_onset i <- incidence(dat, interval = "week") i f <- fit(i[1:20]) f plot(i, fit = f)
As you can see, the incidence_fit
object has a print method and a plot method.
If you want to access individual elements in the $info
element, you can use
the get_info()
function:
get_info(f, "r") get_info(f, "r.conf") get_info(f, "doubling.conf")
This will be important later when we combine several incidence_fit
objects into
a single incidence_fit_list
.
incidence_fit
object from scratchThe incidence_fit
object can be constructed from any model from which you can
derive the daily growth rate, doubling/halving times, predictions, and confidence
intervals. The following three steps show roughly how it is done from model
fitting to construction.
The default model for fit()
is a log-linear model on the intervals between
dates. To fit this model, we will need to create a data frame with the counts
and the midpoints of the intervals:
# ensure all dates have at least one incidence i2 <- i[1:20] i2 <- i2[apply(get_counts(i2), 1, min) > 0] df <- as.data.frame(i2, long = TRUE) df$dates.x <- get_dates(i2, position = "center", count_days = TRUE) head(df) lm1 <- stats::lm(log(counts) ~ dates.x, data = df) lm1
If we compare that to the $model
element produced from fit()
, we can see that
it is identical:
all.equal(f$model, lm1)
$info
list:The $info
list is created directly from the model itself:
r <- stats::coef(lm1)["dates.x"] r.conf <- stats::confint(lm1, "dates.x", 0.95) new.data <- data.frame(dates.x = sort(unique(lm1$model$dates.x))) pred <- exp(stats::predict(lm1, newdata = new.data, interval = "confidence", level = 0.95)) pred <- cbind.data.frame(new.data, pred) info_list <- list( r = r, r.conf = r.conf, doubling = log(2) / r, doubling.conf = log(2) / r.conf, pred = pred ) info_list
the last step is to combine everything into a list and create the object.
origin <- min(get_dates(i2)) info_list$pred$dates <- origin + info_list$pred$dates.x the_fit <- list( lm = lm1, info = info_list, origin = min(get_dates(i2)) ) class(the_fit) <- "incidence_fit" the_fit plot(i, fit = the_fit)
incidence_fit_list
objectThere are several reasons for having multiple fits to a single incidence
object. One may want to have a separate fit for different groups represented in
the object, or one may want to split the fits at the peak of the epidemic. To
aid in plotting and summarizing the different fits, we've created the
incidence_fit_list
class. This class has two defining features:
incidence_fit
objects or
lists containing incidence_fit
objects.incidence_fit
objects in the object. Each list element contains
a vector that defines where an incidence_fit
object is within the
incidence_fit_list
. The reason for this structure is because it is sometimes necessary to nest
lists of incidence_fit
objects within lists. When this happens, accessing
individual elements of the objects cumbersome. To alleviate this, each object
has a distinct path within the named list in the "locations" attribute that
allows one to access the object directly since R allows you to traverse the
elements of a nested list by subsetting with a vector:
l <- list(a = list(b = 1, c = 2),d = list(e = list(f = 3, g = 4), h = 5)) str(l) l[[c("a", "b")]] l[[c("d", "e", "f")]]
The function fit_optim_split()
attempts to find the optimal split point in an
epicurve, producing an incidence_fit_list
object in the $fit
element of the
returned list:
fl <- fit_optim_split(i) fl$fit plot(i, fit = fl$fit)
Here you can see that the object looks very similar to the incidence_fit
object,
but it has extra information. The first thing you may notice is the fact that
both "doubling" and "halving" are shown. This is because the two fits have
different signs for the daily growth rate. The second thing you may notice is
the fact that there is something called attr(x, 'locations')
. This attribute
gives the location of the incidence_fit
objects within the list. We can
illustrate how this works if we look at the structure of the object:
str(fl$fit, max.level = 2)
Internally, all of the methods for incidence_fit_list
use the 'locations'
attribute to navigate:
methods(class = "incidence_fit_list")
For example, it's often useful to extract the growth rate for all models at
once. The get_info()
method allows us to do this easily:
get_info(fl$fit, "r") get_info(fl$fit, "r.conf")
Because doubling or halving is determined by whether or not r
is negative, we
automatically filter out the results that don't make sense, but you can include
them with na.rm = FALSE
:
get_info(fl$fit, "doubling.conf") get_info(fl$fit, "doubling.conf", na.rm = FALSE)
Above, we showed the example of a basic incidence_fit_list
class with two
objects representing the fits before and after the peak of an epicurve. However,
it is often useful evaluate fits for different groups separately. Here, we will
construct an incidence object, but define groups by gender:
gen <- ebola_sim$linelist$gender ig <- incidence(dat, interval = "week", group = gen) plot(ig, border = "grey98")
Now if we calculate an optimal fit split, we will end up with four different fits: two for each defined gender.
fg <- fit_optim_split(ig) plot(ig, fit = fg$fit, border = "grey98", stack = FALSE)
If we look at the fit object, we can see again that it is an incidence_fit_list
but this time with four fits defined.
fg$fit str(fg$fit, max.level = 3)
Notice that the nested lists themselves are of class
incidence_fit_list
.
Now, even though the fits within nested lists, the 'locations' attributes still
defines where they are within the object so that the get_info()
function still
operates normally:
get_info(fg$fit, "r.conf")
If you need to access all the fits easily, a convenience function to flatten the
list is available in get_fit()
:
str(get_fit(fg$fit), max.level = 2)
Because all that defines an incidence_fit_list
is the class definition and
the 'locations' attribute that defines the positions of the incidence_fit
objects within the nesting, then it's also possible to define the output of
fit_optim_split()
as an incidence_fit_list
class:
print(locs <- attributes(fg$fit)$locations) for (i in seq_along(locs)) { locs[[i]] <- c("fit", locs[[i]]) } print(locs) fg.ifl <- fg attributes(fg.ifl)$locations<- locs class(fg.ifl) <- "incidence_fit_list"
Now when we print the object, we can see that it prints only the information
related to the incidence_fit_list
:
fg.ifl
But, we still retain all of the extra information in the list:
str(fg.ifl, max.level = 1) fg.ifl$split fg.ifl$df fg.ifl$plot
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