test_that("pclspiar() is ok", {
ts1 <- window(dataFranses1996[ , "CanadaUnemployment"],
start = c(1960, 1), end = c(1987, 4))
test_piar(ts1, 4, 1, sintercept = TRUE)
pcTest(ts1, "piar", 4, 1, sintercept = TRUE) # same
test_piar(ts1, 4, 1, sintercept = TRUE, sslope = TRUE)
test_piar(ts1, 4, 1)
test_piar(ts1, 4, 1, homoschedastic = TRUE)
parcoef <- rbind(c(0.5, -0.06), c(0.6, -0.08),
c(0.7, -0.1), c(0.2, 0.15) )
picoef1 <- c(0.8, 1.25, 2, 0.5)
parcoef2 <- pi1ar2par(picoef1, parcoef)
picoef2 <- c(4, 0.25, 5, 0.2)
coefper2I2 <- pi1ar2par(picoef2, parcoef2)
expect_identical(coefper2I2, piar2par(picoef2, parcoef2))
expect_identical(coefper2I2, piar2par(matrix(picoef2, ncol = 1), parcoef2))
## from examples for "pcTest-methods"
cu <- pcts(dataFranses1996[ , "CanadaUnemployment"])
cu <- window(cu, start = availStart(cu), end = availEnd(cu))
test_piar(cu, 4, 1, sintercept = TRUE)
pcTest(cu, "piar", 4, 1, sintercept = TRUE)
pcTest(as.numeric(cu), "piar", 4, 1, sintercept = TRUE)
## if(require(partsm)){
## ## same with LRurpar.test from partsm
## LRurpar.test(cu, list(regular = c(0,0,0), seasonal = c(1,0), regvar = 0), p = 1)
## }
pcTest(pcts(nsaauto), "wn")
pcTest(pcts(nsaauto), "piar", p = 1)
## tmpslMat <- slMatrix(rnorm(32), period = 4, maxlag = 7)
acr <- autocorrelations(pcts(nsaauto), maxlag = 7)
acrsl <- slMatrix(as.matrix(acr))
pcTest(acrsl, "pwn", nepoch = nCycles(pcts(nsaauto)))
pcTest(acrsl, "periodicity", nepoch = nCycles(pcts(nsaauto)))
pcTest(pcts(nsaauto), "pwn", maxlag = 4)
pcTest(as.numeric(nsaauto), "pwn", maxlag = 4, nseasons = 4)
pcTest(acrsl, "periodicity", nepoch = nCycles(pcts(nsaauto)))
pcTest(pcts(nsaauto), "wn")
pcTest(pcts(nsaauto), "wn", lag = 3)
## pcTest(matrix(nsaauto, nrow = 4), "wn")
pcTest(pcts(datansa), "pwn", maxlag = 4)
})
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