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test-fitPM.R
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test-fitPM.R
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context("fitPM")
test_that("test fitPM()",
{
set.seed(1234)
x <- arima.sim(list(ar = 0.9), n = 1000)
mx <- matrix(x, nrow = 4)
x_pcts <- pcts(as.numeric(x), nseasons = 4)
expect_error(fitPM(c(1.5, 2, 3, 1), x), "The PAR orders must be non-negative integer numbers")
expect_error(fitPM("dummy", x), "doesn't have a method for 'model' of class character")
expect_error(fitPM(c(3,2,2,2), mx), "multivariate PAR fitting not implemented yet")
proba1 <- fitPM(c(3, 2, 2, 2), as.numeric(mx))
expect_equal_to_reference(proba1, "proba1.RDS")
expect_output(show(proba1))
expect_output(summary(proba1))
expect_error(fitPM(2, mx),
## "unable to find an inherited method for function [.]nSeasons[.] for signature [.]\"matrix\"[.]"
"unable to find an inherited method for function"
)
expect_identical(fitPM(2, x_pcts), fitPM(c(2, 2, 2, 2), as.numeric(mx)))
data(Fraser, package = "pear")
logFraser <- log(Fraser)
## TODO: for now I need whole years;
## !!! However note the typo 'logfraser', the following use 'logFraser'!
logfraser <- ts(logFraser[1:936], frequency = 12)
#### co1_pear <- pear::pear(logFraser, 1)[["phi"]]
# fitPM(as.numeric(logFraser), order = rep(1, 12), period = 12, seasonof1st = 3)
az1 <- fitPM(model = rep(1, 12), as.numeric(logFraser), seasonof1st = 3)
az2 <- fitPM(model = rep(1, 12), as.numeric(logFraser))
#### expect_true(all.equal(as.vector(az1@ar@coef[ , 1]), as.vector(co1_pear[ , 1])))
#### expect_true(all.equal(as.vector(az2@ar@coef[ , 1]), as.vector(co1_pear[ , 1])[c(3:12, 1:2)]))
## pcfr2 <- pcts(dataFranses1996[ , 2 ])
pcfr23 <- pcts(dataFranses1996[ , 2:3])
expect_error(fitPM(model = rep(1, 4), pcfr23), "Multivariate case not implemented yet")
## fitPM(model = rep(1, 4), pcfr23[1]) # tests the method for PeriodicMTS ([] keep MTS class)
## fitPM(model = rep(1, 4), pcfr23[[1]]) # tests the method for PeriodicTS ('[[' drops the 'M')
expect_identical(fitPM(model = rep(1, 4), pcfr23[1]),
fitPM(model = rep(1, 4), pcfr23[[1]]))
x <- arima.sim(list(ar = 0.9), n = 960)
pcx <- pcts(x, nseasons = 4)
mx <- matrix(x, nrow = 4)
pfm1 <- PeriodicArModel(matrix(1:12, nrow = 4), order = rep(3,4), sigma2 = 1)
sipfm1 <- new("SiPeriodicArModel", iorder = 1, siorder = 1, pcmodel = pfm1)
fitPM(sipfm1, mx)
expect_output(show(sipfm1))
d4piar2 <- rbind(c(1,0.5,-0.06), c(1, 0.6, -0.08), c(1, 0.7, -0.1), c(1, 0.2, 0.15))
picoef1 <- c(0.8, 1.25, 2, 0.5)
parcoef1 <- d4piar2[, 2:3]
coef1 <- pi1ar2par(picoef1, parcoef1)
tmpval <- PeriodicArModel(parcoef1)
##pipfm <- PiParModel(piorder = 1, picoef = picoef1, par = tmpval)
pipfm <- new("PiPeriodicArModel", piorder = 1,
picoef = matrix(picoef1, ncol = 1), pcmodel = tmpval)
expect_output(show(pipfm))
perunit <- sim_pc(list(phi = coef1, p = 3, q = 0, period = 4),500)
fitPM(pipfm, perunit)
})
test_that("test mC.ss() works",
{
## examples from mC.ss.Rd
# test0 roots
spec.coz2 <- mcompanion::mcSpec(dim = 5, mo = 4, root1 = c(1,1), order = rep(2,4))
spec.coz2
xxcoz2a <- mC.ss(spec.coz2)
## test0 roots
spec.coz4 <- mcompanion::mcSpec(dim = 5, mo = 4, root1 = c(1,1), order = rep(3,4))
xxcoz4a <- mC.ss(spec.coz4)
## excerpt from
## ~/Documents/Rwork/pctsExperiments/Rsessions/combined upto 2013-12-31 17h36m.Rhistory
spec.co2 <- mcompanion::mcSpec(dim = 5, mo = 4, siorder = 1)
tmp2 <- mC.ss(spec.co2)
## only two iters for testthat
expect_output(mc.res1ssenv2b <- tmp2$env$minimBB(nsaauto, control=list(maxit=2)))
expect_output(tmp2$env$minimBB(nsaauto, control=list(maxit=2)))
expect_output(tmp2$env$minimBBlu(nsaauto, control=list(maxit=2)))
expect_output(tmp2$env$minimBB(nsaauto, control=list(maxit=2), CONDLIK = FALSE))
tmp2$env$minim(nsaauto, control=list(maxit=2))
tmp2$env$minim(nsaauto, control=list(maxit=2), CONDLIK = FALSE)
expect_output(tmp2$env$minimBB(nsaauto, control=list(maxit=2), CONDLIK = FALSE))
mC.ss(spec.co2, generators = TRUE)
tmp2$env$mcparam2optparam()
tmp2$env$mcsigma2(nsaauto)
tmp2$env$mcsigma2(nsaauto, tmp2$env$mcparam2optparam())
mC.ss(spec.co2, init = tmp2$env$mcparam2optparam())
## this chunk was commented out in mC.ss.Rd, old testing with it.
## > xxco.1 <- mC.ss(m1.new, generators = TRUE)
##
## > datansa <- read.csv("nsadata.csv")
## > nsaauto <- ts(datansa$AUTOMOTIVEPRODNSA[113:328], start=c(1947, 1), frequency=4)
##
## > res.xxco.1 <- xxco.1$env$minimBB(nsaauto, control=list(maxit=1000))
##
## condlik is: 32.85753 persd is: 16.96771 10.40725 3.567698 7.426556
## iter: 0 f-value: 32.85753 pgrad: 14.83674
## iter: 10 f-value: 30.21297 pgrad: 0.0007615952
## Successful convergence.
##
## > res.xxco.1$value
## [1] 30.21297
## > res.xxco.1$par
## co.r1 co.r2 co.r3 co.r4
## -0.4069477 -0.5093360 -0.6026860 -0.5174826
## > res.xxco.1
## $par
## co.r1 co.r2 co.r3 co.r4
## -0.4069477 -0.5093360 -0.6026860 -0.5174826
##
## $value
## [1] 30.21297
##
## $gradient
## [1] 9.023893e-06
##
## $fn.reduction
## [1] 2.644559
##
## $iter
## [1] 14
##
## $feval
## [1] 16
##
## $convergence
## [1] 0
##
## $message
## [1] "Successful convergence"
##
## $cpar
## method M
## 2 50
##
## > with(xxco.1$env, model)
## $period
## [1] 4
##
## $p
## [1] 5
##
## $q
## [1] 0
##
## $phi
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.1646497 -1.165471e-16 -4.254923e-17 1 -1.1646497
## [2,] 0.8451102 -2.220446e-16 -5.456035e-17 1 -0.8451102
## [3,] 0.7989768 0.000000e+00 2.220446e-16 1 -0.7989768
## [4,] 1.2716195 -1.110223e-16 -6.058867e-17 1 -1.2716195
##
## > with(xxco.1$env, zapsmall(model$phi))
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.1646497 0 0 1 -1.1646497
## [2,] 0.8451102 0 0 1 -0.8451102
## [3,] 0.7989768 0 0 1 -0.7989768
## [4,] 1.2716195 0 0 1 -1.2716195
})