Regroup observational data into even clumps of individuals.

This function provides a easy way to regroup recorded data into groups of same number of individuals.

clump(object, ...)

# S3 method for intensity
clump(object, unit_size, fun = sum,
  inclusive_unspecified = FALSE, ...)

Arguments

object	An intensity object.
...	Additional arguments to be passed to fun.
unit_size	Size of a group unit. It must be a named vector, with names corresponding to non-observational variables (i.e. space and time variables). If the size of a variable in the data set is not a multiple of the provided value in unit_size, some sampling units (the last ones) will be dropped so that clumps of individuals remain even throughout the data set.
fun	Function used to group observational data together.
inclusive_unspecified	Not yet implemented. Do unspecified mapped variables (different from i and n) need to be included into the bigger possible sampling unit (TRUE) or splited into as many sampling units as possible (FALSE, default)?

Examples

my_incidence <- incidence(tomato_tswv$field_1929)
plot(my_incidence, type = "all")
#> Warning: Computation failed in `stat_summary()`:
#> Hmisc package required for this function

# Different spatial size units:
my_incidence_clumped_1 <- clump(my_incidence, unit_size = c(x = 3, y = 3))
plot(my_incidence_clumped_1, type = "all")
#> Warning: Computation failed in `stat_summary()`:
#> Hmisc package required for this function

my_incidence_clumped_2 <- clump(my_incidence, unit_size = c(x = 4, y = 5))
plot(my_incidence_clumped_2, type = "all")
#> Warning: Computation failed in `stat_summary()`:
#> Hmisc package required for this function

# To get mean disease incidence for each plant over the 3 scoring dates:
my_incidence_clumped_3 <- clump(my_incidence, unit_size = c(t = 3), fun = mean)
plot(my_incidence_clumped_3)