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)
    
})