## ----setup, include = FALSE, echo=FALSE--------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", out.width = NULL ) ## ----packages----------------------------------------------------------------- ## Install extra packages (if needed): # install.packages("folio") # datasets # Load packages library(kairos) ## ----event-model-------------------------------------------------------------- ## Bellanger et al. did not publish the data supporting their demonstration: ## no replication of their results is possible. ## Here is an example using the Zuni dataset from Peeples and Schachner 2012 data("zuni", package = "folio") ## Assume that some assemblages are reliably dated (this is NOT a real example) ## The names of the vector entries must match the names of the assemblages zuni_dates <- c( LZ0569 = 1097, LZ0279 = 1119, CS16 = 1328, LZ0066 = 1111, LZ0852 = 1216, LZ1209 = 1251, CS144 = 1262, LZ0563 = 1206, LZ0329 = 1076, LZ0005Q = 859, LZ0322 = 1109, LZ0067 = 863, LZ0578 = 1180, LZ0227 = 1104, LZ0610 = 1074 ) ## Model the event and accumulation date for each assemblage model <- event(zuni, dates = zuni_dates, rank = 10) ## Extract model coefficients ## (convert results to Gregorian years) coef(model, calendar = CE()) ## Extract residual standard deviation ## (convert results to Gregorian years) sigma(model, calendar = CE()) ## Extract model residuals ## (convert results to Gregorian years) resid(model, calendar = CE()) ## Extract model fitted values ## (convert results to Gregorian years) fitted(model, calendar = CE()) ## ----event-predict------------------------------------------------------------ ## Estimate event dates eve <- predict_event(model, margin = 1, level = 0.95) head(eve) ## Estimate accumulation dates (median) acc <- predict_accumulation(model, level = 0.95) head(acc) ## ----event-plot, fig.width=7, fig.height=7------------------------------------ ## Activity plot plot(model, type = "activity", event = TRUE, select = 1:6) plot(model, type = "activity", event = TRUE, select = "LZ1105") ## Tempo plot plot(model, type = "tempo", select = "LZ1105") ## ----event-refine, warning=FALSE---------------------------------------------- ## Check model variability ## Jackknife fabrics jack <- jackknife(model) head(jack) ## Bootstrap of assemblages boot <- bootstrap(model, n = 30) head(boot)