Quality measures of age estimation

Comparison of estimated age and known age-at-death with the help of several Goodness-of-fit measures. For most of the measures smaller is better. The only exception is corrPearson where larger is better.

Usage

age.comp.summary(
  mcmc_list,
  known_age,
  mean_choice = "Mode",
  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.

mean_choice

a character string of either "Mean", "Median" or "Mode". Default: "Mode".

age_identifier

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

...

Arguments passed on to diagnostic.summary

HDImass

numeric. Value within 0 and 1. Default = 0.95.

gelman_diag

logical. If TRUE, the Gelman-Rubin diagnostics for computing the PSRF is invoked. Default: TRUE.

Value

A data.frame with one row and eight columns with age estimation quality parameters as follows:

• Bias Arithmetic mean of the difference between known and estimated age.

• corrPearson Correlation of known and estimated age.

• corr_p p-value of the correlation of known and estimated age.

• Residual_slope Slope of the regression line of the difference between known and estimated age.

• Inaccuracy Arithmetic mean of the absolute difference between known and estimated age.

• RMSE Root mean square error of known and estimated age.

• TMNLP Test mean log posterior, a local evaluation of the probability density at the point of known age.

• CRPS Continuous ranked probability score, a global evaluation of the probability density at the point of known age.

Examples

if (FALSE) { # interactive()

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

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

  # compute age summary statistics
  age.comp.summary(spitalfields_res, known_age = spitalfields$Age)
}