update

DESCRIPTION

@@ -6,10 +6,15 @@ Authors@R:
            family = "Karakas",
            role = c("aut", "cre"),
            email = "[email protected]")
-Description: Automating time series stationarity tests. This package automates Kwiatkowski–Phillips–Schmidt–Shin (KPSS), Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests and saves the results as a dataframe.
+Description: Automating time series stationarity tests. This package automates Kwiatkowski–Phillips–Schmidt–Shin (KPSS), Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests by calculating every possible situation for lag length, type and significance level (1%, 2.5%, 5% and 10%) and saves the results as a dataframe.
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.1.1
 Depends: R (>= 4.0.0), dplyr, urca
 Imports: dplyr,urca
+Language:en-US
+License: GPL-2
+Suggests: 
+    testthat (>= 3.0.0)
+Config/testthat/edition: 3

NAMESPACE

@@ -1,3 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
-export(summary_stationarity)
+S3method(summary,adf)
+S3method(summary,all)
+S3method(summary,kpss)
+S3method(summary,pp)

R/summary-adf.R

@@ -0,0 +1,45 @@
+#' @title ADF Test Results.
+#'
+#' @description
+#' This function computes ADF statistics and critical values and aggregate them in one dataframe for all possible situations.
+#'
+#' @param model fitted \code{lm(y ~ x)}  object
+#' @param lag Integer lag length. For example, if the value is \code{10}, results will come for lag lengths of \code{1:10}.
+#' @export
+#' @examples
+#' summary.adf(ts_model,10)
+
+summary.adf <- function(model,lag) {
+
+  if (lag>20) {
+    stop("Lag can not be greater than 20.")
+  }
+
+  variables <-c(as.data.frame(model$model),as.data.frame(model$residuals)) %>% as.data.frame()
+
+  adf_type <- c("none","drift","trend")
+  adf = list()
+  for (variable in names(variables)) {
+    for (type in adf_type) {
+      for (i in 1:10) {
+        adf_new <- ur.df(variables[[variable]],type = type, lags = i)
+        adf_result <- as.data.frame(cbind(Test="ADF",Type=type,Variable=variable,Lag=i,"Statistic"=adf_new@teststat[1],as.data.frame(adf_new@cval)[1,])) %>%
+          mutate(Statistic=as.numeric(.data[["Statistic"]]),
+                 "1pct"=as.numeric(.data[["1pct"]]),
+                 "5pct"=as.numeric(.data[["5pct"]]),
+                 "10pct"=as.numeric(.data[["10pct"]]))
+        row.names(adf_result) <- NULL
+        adf_result <- adf_result %>% mutate("2.5pct"="Not avaiable for ADF") %>% relocate("2.5pct", .before = "10pct")
+        adf_result["10% result"] <- ifelse(abs(adf_result[["Statistic"]])>abs(adf_result[["10pct"]]),"pass","fail")
+        adf_result["5% result"] <- ifelse(abs(adf_result[["Statistic"]])>abs(adf_result[["5pct"]]),"pass","fail")
+        adf_result["2.5% result"] <- "Not available for ADF"
+        adf_result["1% result"] <- ifelse(abs(adf_result[["Statistic"]])>abs(adf_result[["1pct"]]),"pass","fail")
+        adf[[paste0(i,type,variable)]] <- adf_result # add it to list
+      }
+    }
+  }
+  adf_full <- do.call(rbind, adf)
+  row.names(adf_full) <- NULL
+  return(adf_full)
+
+}

R/summary_stationarity.R → R/summary-all.R

@@ -7,9 +7,9 @@
 #' @param lag Integer lag length. For example, if the value is \code{10}, results will come for lag lengths of \code{1:10}.
 #' @export
 #' @examples
-#' summary_stationarity(ts_model,10)
+#' summary.all(ts_model,10)
 
-summary_stationarity <- function(model,lag) {
+summary.all <- function(model,lag) {
 
   if (lag>20) {
     stop("Lag can not be greater than 20.")

R/summary-kpss.R

@@ -0,0 +1,45 @@
+#' @title KPSS Test result.
+#'
+#' @description
+#' This function computes KPSS statistics and critical values and aggregate them in one dataframe for all possible situations.
+#'
+#' @param model fitted \code{lm(y ~ x)}  object
+#' @param lag Integer lag length. For example, if the value is \code{10}, results will come for lag lengths of \code{1:10}.
+#' @export
+#' @examples
+#' summary.kpss(ts_model,10)
+
+summary.kpss <- function(model,lag) {
+
+  if (lag>20) {
+    stop("Lag can not be greater than 20.")
+  }
+
+  variables <-c(as.data.frame(model$model),as.data.frame(model$residuals)) %>% as.data.frame()
+
+  kpss_type <-c("mu","tau")
+  kpss = list()
+  for (variable in names(variables)) {
+    for (type in kpss_type) {
+      for (i in 1:lag) {
+        kpss_new <- ur.kpss(variables[[variable]],type = type, use.lag=i)
+        kpss_result <- as.data.frame(cbind(Test="KPSS",Type=type,Variable=variable,Lag=i,"Statistic"=kpss_new@teststat[1],kpss_new@cval)) %>%
+          mutate(Statistic=as.numeric(.data[["Statistic"]]),
+                 "1pct"=as.numeric(.data[["1pct"]]),
+                 "5pct"=as.numeric(.data[["5pct"]]),
+                 "10pct"=as.numeric(.data[["10pct"]]))
+        row.names(kpss_result) <- NULL
+        kpss_result[["Type"]] <- ifelse(kpss_result[["Type"]]=="mu","constant","trend")
+        kpss_result["10% result"] <- ifelse(kpss_result[["Statistic"]]<kpss_result[["10pct"]],"pass","fail")
+        kpss_result["5% result"] <- ifelse(kpss_result[["Statistic"]]<kpss_result[["5pct"]],"pass","fail")
+        kpss_result["2.5% result"] <- ifelse(kpss_result[["Statistic"]]<kpss_result[["2.5pct"]],"pass","fail")
+        kpss_result["1% result"] <- ifelse(kpss_result[["Statistic"]]<kpss_result[["1pct"]],"pass","fail")
+        kpss[[paste0(i,type,variable)]] <- kpss_result # add it to list
+      }
+    }
+  }
+  kpss_full <- do.call(rbind, kpss)
+  row.names(kpss_full) <- NULL
+  return(kpss_full)
+
+}

R/summary-pp.R

@@ -0,0 +1,46 @@
+#' @title PP Test Results.
+#'
+#' @description
+#' This function computes PP statistics and critical values and aggregate them in one dataframe for all possible situations.
+#'
+#' @param model fitted \code{lm(y ~ x)}  object
+#' @param lag Integer lag length. For example, if the value is \code{10}, results will come for lag lengths of \code{1:10}.
+#' @export
+#' @examples
+#' summary.pp(ts_model,10)
+
+summary.pp <- function(model,lag) {
+
+  if (lag>20) {
+    stop("Lag can not be greater than 20.")
+  }
+
+  variables <-c(as.data.frame(model$model),as.data.frame(model$residuals)) %>% as.data.frame()
+
+  pp_type <- c("constant","trend")
+  pp = list()
+  for (variable in names(variables)) {
+    for (type in pp_type) {
+      for (i in 1:10) {
+        pp_new <- ur.pp(variables[[variable]],type = c("Z-tau"), model=type, use.lag=i)
+        pp_result <- as.data.frame(cbind(Test="PP",Type=type,Variable=variable,Lag=i,"Statistic" = pp_new@teststat,pp_new@cval)) %>%
+          mutate(Statistic=as.numeric(.data[["Statistic"]]),
+                 "1pct"=as.numeric(.data[["1pct"]]),
+                 "5pct"=as.numeric(.data[["5pct"]]),
+                 "10pct"=as.numeric(.data[["10pct"]]))
+        row.names(pp_result) <- NULL
+        pp_result <- pp_result %>% mutate("2.5pct"="Not avaiable for PP") %>% relocate("2.5pct", .before = "10pct")
+        pp_result["10% result"] <- ifelse(abs(pp_result[["Statistic"]])>abs(pp_result[["10pct"]]),"pass","fail")
+        pp_result["5% result"] <- ifelse(abs(pp_result[["Statistic"]])>abs(pp_result[["5pct"]]),"pass","fail")
+        pp_result["2.5% result"] <- "Not available for PP"
+        pp_result["1% result"] <- ifelse(abs(pp_result[["Statistic"]])>abs(pp_result[["1pct"]]),"pass","fail")
+        pp[[paste0(i,type,variable)]] <- pp_result # add it to list
+      }
+    }
+  }
+  pp_full <- do.call(rbind, pp)
+  row.names(pp_full) <- NULL
+
+  return(pp_full)
+
+}

stationarityR.Rproj

@@ -0,0 +1,17 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source