The family argument of cpt_detect() names the
distribution a segment is modelled with, so count data, waiting times
and binary series can be segmented with a cost written for them rather
than a Gaussian one. cpt_families() is the table behind it: every
legal combination of method, change_in and family,
and the engine call each becomes.
Value
A tibble with one row per legal combination: method,
change_in, family (NA for a distribution-free
method, which takes no family), engine_call (what is run) and
data (what the family requires of the series: "counts",
"binary", "positive" or NA).
The families
"gaussian"The default of every parametric method. On
"cpm"it also switches from the nonparametric statistics to the Gaussian ones (Student, Bartlett)."poisson"Counts:
change_in = "mean"is a change in the rate. Needs non-negative whole numbers."binomial"Binary outcomes: a change in the success probability. Needs 0/1 data.
"exponential"Waiting times: a change in the rate (the hazard), so
change_in = "mean". Needs positive data."gamma"Positive data with a known shape (pass
shape, default 1): a change in the scale."laplace"Heavy tails: a change in the median and the scale (
change_in = "meanvar")."l1"The absolute-loss cost: a change in the median, robust to outliers (
change_in = "mean").
A single-parameter family has one thing to change, so Poisson,
binomial, exponential and gamma requests use change_in = "mean".
Why an argument and not a model grammar
A grammar that marks the changing parameter
(poisson(rate = NA), normal(mean = NA, sd = 1)) would unify
change_in, family and the formula interface in one
expression, and measured against the fifty methods it fits 42 of them
cleanly. It is deferred to 1.0, where change_in can be replaced
rather than joined by a second idiom, and this table is the first half of
it: change_in = "mean", family = "poisson" already means
poisson(rate = NA) internally.
See also
cpt_detect(), cpt_methods() (its
families column), cpt_simulate() to generate
data from each family.
Examples
cpt_families("pelt")
#> # A tibble: 6 × 5
#> method change_in family engine_call data
#> <chr> <chr> <chr> <chr> <chr>
#> 1 pelt mean gaussian "changepoint::cpt.mean(method = \"PELT\")" NA
#> 2 pelt mean poisson "changepoint::cpt.meanvar(test.stat = \"Po… coun…
#> 3 pelt mean exponential "changepoint::cpt.meanvar(test.stat = \"Ex… posi…
#> 4 pelt mean gamma "changepoint::cpt.meanvar(test.stat = \"Ga… posi…
#> 5 pelt meanvar gaussian "changepoint::cpt.meanvar(method = \"PELT\… NA
#> 6 pelt var gaussian "changepoint::cpt.var(method = \"PELT\")" NA
# every method that can fit a Poisson rate
unique(subset(cpt_families(), family == "poisson")$method)
#> [1] "pelt" "binseg" "segneigh" "amoc" "smuce"
#> [6] "bocpd" "segmented" "fastcpd" "binsegrcpp"
set.seed(1)
counts <- c(rpois(100, 3), rpois(100, 9))
cpt_detect(counts, method = "pelt", family = "poisson")
#> ggcpt (changepoint detection result)
#> Method: pelt
#> Change in: mean
#> Changepoints found: 1
#> CP convention: left
#> Penalty: MBIC
#> Series length: 200
#> Family: poisson
#>
#> Changepoints:
#> # A tibble: 1 × 2
#> cp cp_value
#> <int> <dbl>
#> 1 100 3