## ----loading, echo = FALSE, results = 'hide', message=FALSE, warning=FALSE---- library(PINstimation) ## ----echo=FALSE--------------------------------------------------------------- options(crayon.enabled = TRUE) knitr::knit_hooks$set(output = function(x, options){ paste0( '
',
    fansi::sgr_to_html(x = htmltools::htmlEscape(x), warn = FALSE),
    '
' ) }) ## ----sample.pin.1.1----------------------------------------------------------- pindata <- generatedata_mpin(layers=1, ranges = list(eps.b=c(300, 500), eps.s=c(300,500)), verbose = FALSE) ## ----sample.pin.1.2, results='markup'----------------------------------------- show(pindata) ## ----sample.pin.1.3, results='markup'----------------------------------------- show(pindata@data[1:10, ]) ## ----sample.pin.1.4, results='markup'----------------------------------------- actual <- unlist(pindata@empiricals) show(actual) ## ----sample.pin.1.5, results='markup'----------------------------------------- model <- pin_ea(data=pindata@data, verbose = FALSE) estimates <- model@parameters show(estimates) ## ----sample.pin.1.6, results='markup'----------------------------------------- errors <- abs(actual - estimates) show(errors) ## ----sample.mpin.1.1---------------------------------------------------------- mpindata <- generatedata_mpin(layers=2, ranges = list(eps.b=c(12000, 15000), eps.s=c(12000,15000)), verbose = FALSE) ## ----sample.mpin.1.2, results='markup'---------------------------------------- show(mpindata) ## ----sample.mpin.1.3, results='markup'---------------------------------------- show(mpindata@data[1:10, ]) ## ----sample.mpin.1.4, results='markup'---------------------------------------- actualmpin <- unlist(mpindata@emp.pin) show(actualmpin) ## ----sample.mpin.1.5, results='markup'---------------------------------------- model_ml <- mpin_ml(data=mpindata@data, verbose = FALSE) model_ecm <- mpin_ecm(data=mpindata@data, verbose = FALSE) mlmpin <- model_ml@mpin ecmpin <- model_ecm@mpin estimates <- setNames(c(mlmpin, ecmpin), c("ML", "ECM")) show(estimates) ## ----sample.mpin.1.6, results='markup'---------------------------------------- errors <- abs(actualmpin - estimates) show(errors) ## ----sample.mpin.1.7, results='markup'---------------------------------------- size <- 10 collection <- generatedata_mpin(series = size, layers = 3, verbose = FALSE) show(collection) ## ----sample.mpin.1.8, results='markup'---------------------------------------- accuracy <- devmpin <- 0 for (i in 1:size) { sdata <- collection@datasets[[i]] model <- mpin_ml(sdata@data, xtraclusters = 3, verbose=FALSE) accuracy <- accuracy + (sdata@layers == model@layers) devmpin <- devmpin + abs(sdata@emp.pin - model@mpin) } cat('The accuracy of layer detection: ', paste0(accuracy*(100/size),"%.\n"), sep="") cat('The average error in MPIN estimates: ', devmpin/size, ".\n", sep="") ## ----sample.adjpin.1.1-------------------------------------------------------- adjpindatasets <- generatedata_adjpin(series = 10, ranges = list(eps.b=c(10000, 15000), eps.s=c(10000,15000)), verbose = FALSE) ## ----sample.adjpin.1.2, results='markup'-------------------------------------- show(adjpindatasets) ## ----sample.adjpin.1.3, results='markup'-------------------------------------- adjpindata <- adjpindatasets@datasets[[1]] show(adjpindata) ## ----sample.adjpin.1.4, results='markup'-------------------------------------- actualpins <- unlist(adjpindata@emp.pin) show(actualpins) ## ----sample.adjpin.1.5, results='markup'-------------------------------------- model_ml <- adjpin(data=adjpindata@data, method = "ML", verbose = FALSE) model_ecm <- adjpin(data=adjpindata@data, method = "ECM", verbose = FALSE) mlpins <- c(model_ml@adjpin, model_ml@psos) ecmpins <- c(model_ecm@adjpin, model_ecm@psos) estimates <- rbind(mlpins, ecmpins) colnames(estimates) <- c("adjpin", "psos") rownames(estimates) <- c("ML", "ECM") show(estimates) ## ----sample.adjpin.1.6, results='markup'-------------------------------------- errors <- abs(estimates - rbind(actualpins, actualpins)) show(errors)