Details of the incidence_fit class

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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.

Structure of an incidence_fit object

An incidence_fit object contains three elements:

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.

Building an incidence_fit object from scratch

The 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.

Step 1: create the model

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)

Step 2: creation of the $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

Step 3: combine lists and create object

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)

Structure of an incidence_fit_list object

There 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:

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")]]

Example: A tale of two fits

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)

Example: Nested incidence_fit

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 evaulate 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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incidence documentation built on Aug. 25, 2018, 1:03 a.m.