fix minor bugs and add more tests

R/autocovariances.R

@@ -236,7 +236,8 @@ partialAutocorrelations <- function(x, maxlag, lag_0 = TRUE, ...){
             ## TODO: this may fail if 'x' doesn't contain info about autocovariances.
             ##    Maybe just put NA instead?
             wrk <- autocovariances(x, maxlag = 0, ...)
-            res <- as(obj, "FlexibleLagged")
+            ## BUGFIX: was: res <- as(obj, "FlexibleLagged")
+            res <- new("FlexibleLagged", data = pacr)
             res[[0]] <- wrk[[0]]
             res
     }

tests/testthat/test-Old_sarima.R

@@ -0,0 +1,32 @@
+## TODO: need proper tests here
+test_that("functions in sarima.R work ok", {
+    ## define a seasonal ARIMA model
+    m1 <- new("SarimaModel", iorder = 1, siorder = 1, ma = -0.3, sma = -0.1, nseasons = 12)
+
+    model0 <- modelCoef(m1, "ArmaModel")
+    model1 <- as(model0, "list")
+
+    ap.1 <- xarmaFilter(model1, x = AirPassengers, whiten = TRUE)
+    ap.2 <- xarmaFilter(model1, x = AirPassengers, eps = ap.1, whiten = FALSE)
+    ap <- AirPassengers
+    ap[-(1:13)] <- 0 # check that the filter doesn't use x, except for initial values.
+    ap.2a <- xarmaFilter(model1, x = ap, eps = ap.1, whiten = FALSE)
+    ap.2a - ap.2 ## indeed = 0
+    ##ap.3 <- xarmaFilter(model1, x = list(init = AirPassengers[1:13]), eps = ap.1, whiten = TRUE)
+
+    ## now set some non-zero initial values for eps
+    eps1 <- numeric(length(AirPassengers))
+    eps1[1:13] <- rnorm(13)
+    ap.A <- xarmaFilter(model1, x = AirPassengers, eps = eps1, whiten = TRUE)
+    ap.Ainv <- xarmaFilter(model1, x = ap, eps = ap.A, whiten = FALSE)
+    AirPassengers - ap.Ainv # = 0
+
+    ## compare with sarima.f (an old function)
+    ## compute predictions starting at from = 14
+    pred1 <- sarima.f(past = AirPassengers[1:13], n = 131, ar = model1$ar, ma = model1$ma)
+    pred2 <- xarmaFilter(model1, x = ap, whiten = FALSE)
+    pred2 <- pred2[-(1:13)]
+    all(pred1 == pred2) ##TRUE
+    expect_true(all(pred1 == pred2))
+
+})

tests/testthat/test-autocovariances.R

@@ -1,11 +1,15 @@
 test_that("functions in autocorrelations.org work ok", {
+    acv1 <- autocovariances(list(ar = c(0.5), ma = c(0.8), sigma2 = 2), maxlag = 5)
+    expect_true(is(acv1, "Autocovariances"))
+
     v1 <- rnorm(100)
     autocorrelations(v1)
     v1.acf <- autocorrelations(v1, maxlag = 10)
 
     v1.acf[1:10] # drop lag zero value (and the class)
     autocorrelations(v1, maxlag = 10, lag_0 = FALSE) # same
 
+    expect_output(show(autocorrelations(v1, maxlag = 10) ))
     expect_output(show(autocorrelations(v1, maxlag = 10, lag_0 = FALSE) ))
     expect_output(show(partialAutocorrelations(v1)              ))
     expect_output(show(partialAutocorrelations(v1, maxlag = 10) ))
@@ -18,4 +22,30 @@ test_that("functions in autocorrelations.org work ok", {
     expect_output(show(partialVariances(v1, maxlag = 6)       ))
     ## pv1 <- partialVariances(v1)
 
+    expect_true(is.numeric(partialAutocorrelations(v1, lag_0 = FALSE)))
+    partialAutocorrelations(v1, lag_0 = "var")
+
+    ##  
+    n <- 5000
+    x <- sarima:::rgarch1p1(n, alpha = 0.3, beta = 0.55, omega = 1, n.skip = 100)
+    x.acf <- autocorrelations(x)
+    x.pacf <- partialAutocorrelations(x)
+
+    acfGarchTest(x.acf, x = x, nlags = c(5,10,20))
+    acfGarchTest(x.pacf, x = x, nlags = c(5,10,20))
+
+    # do not compute CI's:
+    acfGarchTest(x.pacf, x = x, nlags = c(5,10,20), interval = NULL)
+
+    ## ## plot methods call acfGarchTest() suitably if 'x' is given:
+    ## plot(x.acf, data = x)
+    ## plot(x.pacf, data = x)
+
+    ## ## use 90% limits:
+    ## plot(x.acf, data = x, interval = 0.90)
+
+    acfWnTest(x.acf, x = x, nlags = c(5,10,20))
+    nvarOfAcfKP(x, maxlag = 20)
+    whiteNoiseTest(x.acf, h0 = "arch-type", x = x, nlags = c(5,10,20))
+
 })

tests/testthat/test-fkf.R

@@ -0,0 +1,3 @@
+test_that("temporary disable FKF, see fkf.R", {
+    expect_error(fkf(), "FKF methods are not currently available")
+})