Sequential cumulative binomial test

The cumulative binomial test asserts if the expected coverage, i.e. the percentage of known ages within the highest density intervals, is within the confidence interval of the realized coverage. This wrapper function allows to run this test sequentially, i.e. with a sequence of expected coverage levels, at a confidence level of 0.95.

Usage

sequential.binom.test(
  mcmc_list,
  known_age,
  HDImass = 0.95,
  age_identifier = "age.s"
)

Arguments

mcmc_list

MCMC output from coda chains.

known_age

a vector of known age-at-death. NAs are allowed and those entries will subsequently be ignored.

HDImass

a numeric or a vector with the probability range.

age_identifier

a character string of either "age.s" or "age.s_c" to select the uncalibrated or calibrated age estimates. Default: "age.s".

Value

A dataframe with the number of rows equaling the length of the parameter HDImass and six columns as follows:

• coverage Expected coverage.

• n_in Absolute number of known ages within the highest density intervals.

• perc Realized coverage.

• CI_low Lower limit of the confidence interval for the realized coverage.

• CI_up Upper limit of the confidence interval for the realized coverage.

• p_value p-value of the binomial test. If significant, the expected coverage is outside of the confidence intervals of the realized coverage.

Examples

if (FALSE) { # interactive()

  # select Spitalfields data with multiple traits and convert to matrix
  spitalfields_traits <- spitalfields[,c(2:6)]

  # example with multinormal likelihood, please be patient
  spitalfields_res <- bay.ta(algorithm = "mnorm",
  method = spitalfields_traits)

  # compute sequential binomial tests at expected probability levels 0.75
  # and 0.95
  sequential.binom.test(spitalfields_res, known_age = spitalfields$Age,
  HDImass = c(0.75, 0.95))
}