Last updated on 2023-11-28 09:52:04 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.15.6 | 37.38 | 271.89 | 309.27 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.15.7 | 35.33 | 200.60 | 235.93 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.15.7 | 396.96 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 0.15.7 | 220.67 | OK | |||
| r-devel-windows-x86_64 | 0.15.7 | 32.00 | 205.00 | 237.00 | OK | |
| r-patched-linux-x86_64 | 0.15.6 | 49.29 | 265.85 | 315.14 | OK | |
| r-release-linux-x86_64 | 0.15.7 | 44.60 | 265.24 | 309.84 | OK | |
| r-release-macos-arm64 | 0.15.7 | 101.00 | ERROR | |||
| r-release-macos-x86_64 | 0.15.7 | 241.00 | ERROR | |||
| r-release-windows-x86_64 | 0.15.7 | 34.00 | 242.00 | 276.00 | OK | |
| r-oldrel-macos-arm64 | 0.15.7 | 96.00 | ERROR | |||
| r-oldrel-macos-x86_64 | 0.15.5 | 129.00 | NOTE | |||
| r-oldrel-windows-x86_64 | 0.15.7 | 40.00 | 236.00 | 276.00 | OK |
Version: 0.15.7
Check: examples
Result: ERROR
Running examples in ‘pcts-Ex.R’ failed
The error most likely occurred in:
> ### Name: fitPM
> ### Title: Fit periodic time series models
> ### Aliases: fitPM fitPM-methods fitPM,ANY,ANY-method
> ### fitPM,mcSpec,ANY-method fitPM,numeric,ANY-method
> ### fitPM,PeriodicArModel,ANY-method
> ### fitPM,PeriodicArModel,PeriodicMTS-method
> ### fitPM,PeriodicArModel,PeriodicTS-method
> ### fitPM,PiPeriodicArModel,ANY-method fitPM,SiPeriodicArModel,ANY-method
> ### Keywords: pcts methods
>
> ### ** Examples
>
> ## newm1 <- list(phi = matrix(1:12, nrow=4), p=rep(3,4), period=4, si2 = rep(1,4))
> ## new_pfm1 <- PeriodicFilterModel(newm1, intercept=0)
>
> ## generate some data;
> set.seed(1234)
> simts1 <- pcts(rnorm(1024), nseasons = 4)
>
> fitPM(c(3,3,3,3), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
> fitPM(3, simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
> ## the fit on the underlying data is equivalent.
> fitPM(c(3,3,3,3), as.numeric(simts1))
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
>
> ## equivalently, use a PAR(3,3,3,3) model for argument 'model'
> ## here the coefficients of pfm1 are ignored, since the estimation is linear.
> pfm1 <- PeriodicArModel(matrix(1:12, nrow = 4), order = rep(3,4), sigma2 = 1)
> pfm1
An object of class "PeriodicArModel"
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1 1 1 1
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 1 5 9
S2 2 6 10
S3 3 7 11
S4 4 8 12
> ## these give same results as above
> fitPM(pfm1, simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
> fitPM(pfm1, as.numeric(simts1))
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
>
> fitPM(c(1,1,1,1), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.075896 0.978132 0.9917217 0.91232
Periodic order: AR(1,1,1,1)
ar1
S1 0.108486672
S2 0.001318166
S3 -0.041828251
S4 0.036776157
number of obs. for each season:
256 256 256 256
> fitPM(c(3,2,2,1), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060947 0.9717132 0.9917187 0.91232
Periodic order: AR(3,2,2,1)
ar1 ar2 ar3
S1 0.113395504 -0.122778518 -0.0004315542
S2 -0.006365449 0.084234949 NA
S3 -0.041825811 -0.001666751 NA
S4 0.036776157 NA NA
number of obs. for each season:
256 256 256 256
> fitPM(c(3,2,2,2), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9717132 0.9917187 0.9074826
Periodic order: AR(3,2,2,2)
ar1 ar2 ar3
S1 0.113967280 -0.122469184 0.007590031
S2 -0.006365449 0.084234949 NA
S3 -0.041825811 -0.001666751 NA
S4 0.033877417 -0.070384980 NA
number of obs. for each season:
256 256 256 256
>
> pdSafeParOrder(c(3,2,2,1))
[1] 3 2 2 2
> pdSafeParOrder(rev(c(3,2,2,1)))
[1] 1 2 2 3
>
> x <- arima.sim(list(ar = 0.9), n = 960)
> pcx <- pcts(x, nseasons = 4)
> mx <- matrix(x, nrow = 4)
>
> ##pc.acf(mx)
> ##pc.acf(mx, maxlag=10)
> ## TODO: avoid the warning when length ot the time series is not multiple
> autocovariances(t(mx), maxlag = 6, nseasons = 4)
An object of class "Lagged2d"
Slot *data*:
Lag_0 Lag_1 Lag_2 Lag_3 Lag_4 Lag_5 Lag_6
4.413696 3.698001 3.444325 3.083434 2.981296 2.740127 2.443547
5.256342 4.480398 3.791234 3.376241 2.959491 2.945734 2.704898
4.544553 4.226788 3.573730 2.787481 2.179323 1.972540 2.118425
4.744290 4.149401 3.620436 3.039397 2.267672 1.595720 1.419777
6.188442 4.913592 4.132992 3.413550 2.893992 1.944995 1.202820
5.079685 5.095852 4.020852 3.462512 2.909262 2.517747 1.724930
4.452634 4.097511 4.466851 3.581613 2.929054 2.568013 2.159905
4.028403 3.766262 3.375314 3.895400 3.027806 2.462040 2.266185
4.256483 3.789805 3.519559 3.181409 3.595804 2.723855 2.312791
5.064014 4.082757 3.780803 3.857862 3.271179 3.849951 2.962659
4.610889 4.241189 3.677709 3.378335 3.220566 2.679142 3.078473
4.492845 4.083564 3.715123 3.132441 2.782178 2.780713 2.430421
4.759835 4.221811 3.785254 3.334577 2.753388 2.379137 2.523684
4.176337 3.914353 3.379820 3.318057 2.895464 2.089529 1.813878
4.256341 3.795704 3.522276 3.120697 3.030260 2.548401 1.864357
4.123065 3.730340 3.387307 3.057720 2.614952 2.406073 2.003338
> autocovariances(t(mx))
An object of class "SampleAutocovariances"
, , Lag_0
4.940244 4.425333 3.856613 3.312517
4.425333 4.931782 4.153459 3.576585
3.856613 4.153459 4.586589 4.037720
3.312517 3.576585 4.037720 4.444731
, , Lag_1
3.053109 3.270405 3.765441 4.205794
2.830618 3.009076 3.520014 3.771935
2.262606 2.432067 2.839903 3.242108
1.928944 2.007498 2.285101 2.672126
, , Lag_2
1.754047 1.767143 2.068452 2.409498
1.430174 1.496301 1.828329 2.244051
1.298815 1.204518 1.458011 1.818073
1.238070 1.181317 1.385222 1.602964
, , Lag_3
1.3489948 1.2683819 1.4101905 1.573585
0.9844681 0.8640092 1.0299877 1.269531
0.9647423 0.9969533 0.9680465 1.158972
1.1158544 1.1012862 1.0621654 1.144140
, , Lag_4
1.0681173 1.1595254 1.0799309 1.1913186
0.8013338 0.9328923 0.7987529 0.8834352
0.8382119 0.9644516 0.8820007 0.8776038
0.9012098 1.0414786 1.0527977 0.9306610
, , Lag_5
0.8764845 1.0569533 1.0166630 0.9120474
0.8499661 1.0786949 0.8387545 0.7325839
0.6867834 0.9726713 0.8366954 0.8057214
0.8406734 1.0936087 0.8225248 0.8450239
, , Lag_6
0.9444448 1.1035309 0.8113775 0.8291835
1.1822682 1.2821839 1.0012574 0.8592283
0.8407831 0.8361079 0.6282596 0.6109829
0.8258035 0.7665833 0.6435341 0.6931841
, , Lag_7
0.8689502 0.7994249 0.7699847 0.7690557
1.0024370 0.9676477 0.9242707 0.9797624
0.6284261 0.5784530 0.5320890 0.5642380
0.5484201 0.5434399 0.4715212 0.5167750
, , Lag_8
0.3468684 0.4347957 0.4606310 0.4804946
0.6027137 0.5625705 0.6254156 0.6782344
0.4822539 0.4593642 0.4413930 0.4271425
0.4255004 0.4902419 0.4739370 0.3877285
, , Lag_9
0.2562200 0.3388748 0.2662562 0.2117790
0.3822483 0.4504162 0.3251202 0.3949501
0.1804663 0.3399479 0.2212054 0.3085179
0.1899286 0.3206453 0.1264308 0.1618857
, , Lag_10
0.15180484 0.1875179 -0.03011971 -0.01144414
0.05629723 0.1683812 -0.02502021 0.03307518
-0.08409547 0.1247324 -0.20909008 -0.16511840
-0.07978351 0.1421987 -0.13790670 -0.11767234
, , Lag_11
-0.11875727 0.136673414 -0.12715441 -0.1241331
-0.13047205 0.071926993 -0.12785582 -0.1132037
-0.08908832 -0.009374151 -0.14856401 -0.1563577
-0.04172053 0.093328912 0.04497078 -0.1002592
, , Lag_12
-0.0005362661 0.055885198 -0.06454129 -0.12617478
-0.0837065154 -0.030487010 -0.12492654 -0.05076665
-0.1603845066 -0.092526648 -0.18793024 -0.07192433
-0.0329581327 -0.002391903 -0.07344575 0.04800647
, , Lag_13
0.110795032 0.08497155 -0.10692939 0.039275700
0.067135202 0.08472719 -0.08162884 -0.013675882
0.003970013 0.07475564 -0.12919526 -0.199029101
0.117818973 0.25836952 0.01765533 0.005315572
, , Lag_14
0.2569690 0.4167017 0.1750001 0.099989082
0.2811720 0.3627738 0.2535473 0.142481793
0.2898364 0.3371195 0.1645728 0.008941963
0.2990899 0.3708633 0.1952323 0.025439383
, , Lag_15
0.3366081 0.3613924 0.3104424 0.1448003
0.4606357 0.4791444 0.4525622 0.2297306
0.3597465 0.3331262 0.3165522 0.1907237
0.3781567 0.3046630 0.2260963 0.2302541
, , Lag_16
0.3386886 0.2301517 0.1967166 0.2457013
0.4153102 0.2978708 0.3316045 0.3155847
0.3095268 0.2480752 0.2709355 0.3258925
0.2947141 0.2232702 0.2175240 0.3021335
, , Lag_17
0.3556765 0.2901412 0.2181435 0.3035475
0.3473894 0.3594809 0.3021085 0.3914716
0.3700382 0.4697207 0.3183025 0.4731177
0.2458445 0.4474799 0.2438784 0.4462941
Slot n:
[1] 240
Slot varnames:
[1] "Series 1" "Series 2" "Series 3" "Series 4"
Slot objectname: x
>
> ##It is an error to have more columns than rows.
> ## autocovariances(mx, maxlag = 6, nseasons = 4)
> ## autocovariances(mx)
>
> num2pcpar(mx, c(1,1,1,1), period = 4)
$mean
[1] 0.2212892 0.2610529 0.2222639 0.1751942
$coef
An object of class "slMatrix"
Slot "m":
lag
season 0 1
1 1 -0.9462426
2 1 -0.8957722
3 1 -0.8421822
4 1 -0.8803319
$scale
[1] 0.9800724 0.9837131 1.0433693 0.9435026
> num2pcpar(mx, c(3,3,3,3), period = 4)
$mean
[1] 0.2212892 0.2610529 0.2222639 0.1751942
$coef
An object of class "slMatrix"
Slot "m":
lag
season 0 1 2 3
1 1 -0.9962631 0.009726434 0.05118143
2 1 -0.9003005 0.144883523 -0.15588601
3 1 -0.7223822 -0.177308198 0.05138492
4 1 -0.9444632 0.052370207 0.01986882
$scale
[1] 0.9768186 0.9723284 1.0339197 0.9404123
>
> sipfm1 <- new("SiPeriodicArModel", iorder = 1, siorder = 1, pcmodel = pfm1)
> sipfm1
An object of class "SiPeriodicArModel"
iorder: 1, siorder: 1
PAR part of the model, An object of class "PeriodicArModel"
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1 1 1 1
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 1 5 9
S2 2 6 10
S3 3 7 11
S4 4 8 12
> fitPM(sipfm1, mx)
An object of class "SiPeriodicArModel"
iorder: 1, siorder: 1
PAR part of the model, An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1.900178 2.014823 2.512701 1.919159
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.03633172 0.02831852 0.02492281
S2 -0.02263934 -0.21176922 0.09351833
S3 -0.17347841 0.01862405 0.13451737
S4 0.02770379 0.11071311 0.07843985
number of obs. for each season:
238 238 238 238
> pfm1
An object of class "PeriodicArModel"
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1 1 1 1
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 1 5 9
S2 2 6 10
S3 3 7 11
S4 4 8 12
>
>
> ## experiments and testing
> fit1 <- fitPM(c(3,3,3,3), simts1)
> fit1_mf <- new("MultiFilter", coef = fit1@ar@coef)
> vs <- mcompanion::mf_VSform(fit1_mf, form = "I")
> tmp <- mcompanion::VAR2pcfilter(vs$Phi[ , -4],
+ Phi0inv = vs$Phi0inv, D = fit1@sigma2, what = "")
> names(tmp) # "pcfilter" "var" "Uform"
[1] "pcfilter" "var" "Uform"
> tmp$var
[1] 0.9074826 0.9903078 0.9714497 1.0608910
> zapsmall(tmp$pcfilter)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.11396728 -0.12246918 0.00759003 0 0 0
[2,] -0.00450794 0.08337720 0.01641127 0 0 0
[3,] -0.04491259 -0.00527692 0.03962269 0 0 0
[4,] 0.03387766 -0.07038516 0.00013228 0 0 0
> fit1@ar@coef
lag
season 1 2 3
1 0.11396728 -0.122469184 0.0075900305
2 -0.00450794 0.083377197 0.0164112748
3 -0.04491259 -0.005276924 0.0396226884
4 0.03387766 -0.070385164 0.0001322766
> all.equal(tmp$pcfilter[ , 1:3], fit1@ar@coef, check.attributes = FALSE) # TRUE
[1] TRUE
> tmp$Uform
$Sigma
[1] 1.0608910 0.9714497 0.9903078 0.9074826
$U0
[,1] [,2] [,3] [,4]
[1,] 1 -0.03387766 0.07038516 -0.0001322766
[2,] 0 1.00000000 0.04491259 0.0052769241
[3,] 0 0.00000000 1.00000000 0.0045079396
[4,] 0 0.00000000 0.00000000 1.0000000000
$U
[,1] [,2] [,3]
[1,] -1.829718e-19 6.802028e-20 -3.263662e-22
[2,] 3.962269e-02 4.045944e-20 2.495333e-21
[3,] 8.337720e-02 1.641127e-02 -1.224348e-21
[4,] 1.139673e-01 -1.224692e-01 7.590031e-03
$U0inv
[,1] [,2] [,3] [,4]
[1,] 1 0.03387766 -0.07190670 0.0002776578
[2,] 0 1.00000000 -0.04491259 -0.0050744608
[3,] 0 0.00000000 1.00000000 -0.0045079396
[4,] 0 0.00000000 0.00000000 1.0000000000
$perm
[1] 4 3 2 1
> fit1@sigma2
[1] 1.0608910 0.9714497 0.9903078 0.9074826
>
> ## both give the matrix Sigma for the "I" form
> identical(
+ vs$Phi0inv %*% diag(fit1@sigma2) %*% t(vs$Phi0inv)
+ ,
+ tmp$Uform$U0inv %*% diag(tmp$Uform$Sigma) %*% t(tmp$Uform$U0inv)
+ ) # TRUE
[1] TRUE
>
> ## no, this is a different matrix
> var1_mat <- cbind(vs$Phi0, # identity matrix
+ - vs$Phi) # drop trailing zero columns?
> var1_mat <- mcompanion::mCompanion(var1_mat)
> var1_Sigma <- vs$Phi0inv %*% diag(fit1@sigma2) %*% t(vs$Phi0inv)
> abs(eigen(diag(nrow(var1_mat)) - var1_mat)$values)
Error in getClassDef(x@superClass, package = packageSlot(x))@virtual :
no applicable method for `@` applied to an object of class "NULL"
Calls: eigen ... .selectSuperClasses -> vapply -> FUN -> isVirtualExt
Execution halted
Flavor: r-release-macos-arm64
Version: 0.15.7
Check: examples
Result: ERROR
Running examples in ‘pcts-Ex.R’ failed
The error most likely occurred in:
> ### Name: fitPM
> ### Title: Fit periodic time series models
> ### Aliases: fitPM fitPM-methods fitPM,ANY,ANY-method
> ### fitPM,mcSpec,ANY-method fitPM,numeric,ANY-method
> ### fitPM,PeriodicArModel,ANY-method
> ### fitPM,PeriodicArModel,PeriodicMTS-method
> ### fitPM,PeriodicArModel,PeriodicTS-method
> ### fitPM,PiPeriodicArModel,ANY-method fitPM,SiPeriodicArModel,ANY-method
> ### Keywords: pcts methods
>
> ### ** Examples
>
> ## newm1 <- list(phi = matrix(1:12, nrow=4), p=rep(3,4), period=4, si2 = rep(1,4))
> ## new_pfm1 <- PeriodicFilterModel(newm1, intercept=0)
>
> ## generate some data;
> set.seed(1234)
> simts1 <- pcts(rnorm(1024), nseasons = 4)
>
> fitPM(c(3,3,3,3), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
> fitPM(3, simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
> ## the fit on the underlying data is equivalent.
> fitPM(c(3,3,3,3), as.numeric(simts1))
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
>
> ## equivalently, use a PAR(3,3,3,3) model for argument 'model'
> ## here the coefficients of pfm1 are ignored, since the estimation is linear.
> pfm1 <- PeriodicArModel(matrix(1:12, nrow = 4), order = rep(3,4), sigma2 = 1)
> pfm1
An object of class "PeriodicArModel"
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1 1 1 1
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 1 5 9
S2 2 6 10
S3 3 7 11
S4 4 8 12
> ## these give same results as above
> fitPM(pfm1, simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
> fitPM(pfm1, as.numeric(simts1))
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
>
> fitPM(c(1,1,1,1), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.075896 0.978132 0.9917217 0.91232
Periodic order: AR(1,1,1,1)
ar1
S1 0.108486672
S2 0.001318166
S3 -0.041828251
S4 0.036776157
number of obs. for each season:
256 256 256 256
> fitPM(c(3,2,2,1), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060947 0.9717132 0.9917187 0.91232
Periodic order: AR(3,2,2,1)
ar1 ar2 ar3
S1 0.113395504 -0.122778518 -0.0004315542
S2 -0.006365449 0.084234949 NA
S3 -0.041825811 -0.001666751 NA
S4 0.036776157 NA NA
number of obs. for each season:
256 256 256 256
> fitPM(c(3,2,2,2), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9717132 0.9917187 0.9074826
Periodic order: AR(3,2,2,2)
ar1 ar2 ar3
S1 0.113967280 -0.122469184 0.007590031
S2 -0.006365449 0.084234949 NA
S3 -0.041825811 -0.001666751 NA
S4 0.033877417 -0.070384980 NA
number of obs. for each season:
256 256 256 256
>
> pdSafeParOrder(c(3,2,2,1))
[1] 3 2 2 2
> pdSafeParOrder(rev(c(3,2,2,1)))
[1] 1 2 2 3
>
> x <- arima.sim(list(ar = 0.9), n = 960)
> pcx <- pcts(x, nseasons = 4)
> mx <- matrix(x, nrow = 4)
>
> ##pc.acf(mx)
> ##pc.acf(mx, maxlag=10)
> ## TODO: avoid the warning when length ot the time series is not multiple
> autocovariances(t(mx), maxlag = 6, nseasons = 4)
An object of class "Lagged2d"
Slot *data*:
Lag_0 Lag_1 Lag_2 Lag_3 Lag_4 Lag_5 Lag_6
4.413696 3.698001 3.444325 3.083434 2.981296 2.740127 2.443547
5.256342 4.480398 3.791234 3.376241 2.959491 2.945734 2.704898
4.544553 4.226788 3.573730 2.787481 2.179323 1.972540 2.118425
4.744290 4.149401 3.620436 3.039397 2.267672 1.595720 1.419777
6.188442 4.913592 4.132992 3.413550 2.893992 1.944995 1.202820
5.079685 5.095852 4.020852 3.462512 2.909262 2.517747 1.724930
4.452634 4.097511 4.466851 3.581613 2.929054 2.568013 2.159905
4.028403 3.766262 3.375314 3.895400 3.027806 2.462040 2.266185
4.256483 3.789805 3.519559 3.181409 3.595804 2.723855 2.312791
5.064014 4.082757 3.780803 3.857862 3.271179 3.849951 2.962659
4.610889 4.241189 3.677709 3.378335 3.220566 2.679142 3.078473
4.492845 4.083564 3.715123 3.132441 2.782178 2.780713 2.430421
4.759835 4.221811 3.785254 3.334577 2.753388 2.379137 2.523684
4.176337 3.914353 3.379820 3.318057 2.895464 2.089529 1.813878
4.256341 3.795704 3.522276 3.120697 3.030260 2.548401 1.864357
4.123065 3.730340 3.387307 3.057720 2.614952 2.406073 2.003338
> autocovariances(t(mx))
An object of class "SampleAutocovariances"
, , Lag_0
4.940244 4.425333 3.856613 3.312517
4.425333 4.931782 4.153459 3.576585
3.856613 4.153459 4.586589 4.037720
3.312517 3.576585 4.037720 4.444731
, , Lag_1
3.053109 3.270405 3.765441 4.205794
2.830618 3.009076 3.520014 3.771935
2.262606 2.432067 2.839903 3.242108
1.928944 2.007498 2.285101 2.672126
, , Lag_2
1.754047 1.767143 2.068452 2.409498
1.430174 1.496301 1.828329 2.244051
1.298815 1.204518 1.458011 1.818073
1.238070 1.181317 1.385222 1.602964
, , Lag_3
1.3489948 1.2683819 1.4101905 1.573585
0.9844681 0.8640092 1.0299877 1.269531
0.9647423 0.9969533 0.9680465 1.158972
1.1158544 1.1012862 1.0621654 1.144140
, , Lag_4
1.0681173 1.1595254 1.0799309 1.1913186
0.8013338 0.9328923 0.7987529 0.8834352
0.8382119 0.9644516 0.8820007 0.8776038
0.9012098 1.0414786 1.0527977 0.9306610
, , Lag_5
0.8764845 1.0569533 1.0166630 0.9120474
0.8499661 1.0786949 0.8387545 0.7325839
0.6867834 0.9726713 0.8366954 0.8057214
0.8406734 1.0936087 0.8225248 0.8450239
, , Lag_6
0.9444448 1.1035309 0.8113775 0.8291835
1.1822682 1.2821839 1.0012574 0.8592283
0.8407831 0.8361079 0.6282596 0.6109829
0.8258035 0.7665833 0.6435341 0.6931841
, , Lag_7
0.8689502 0.7994249 0.7699847 0.7690557
1.0024370 0.9676477 0.9242707 0.9797624
0.6284261 0.5784530 0.5320890 0.5642380
0.5484201 0.5434399 0.4715212 0.5167750
, , Lag_8
0.3468684 0.4347957 0.4606310 0.4804946
0.6027137 0.5625705 0.6254156 0.6782344
0.4822539 0.4593642 0.4413930 0.4271425
0.4255004 0.4902419 0.4739370 0.3877285
, , Lag_9
0.2562200 0.3388748 0.2662562 0.2117790
0.3822483 0.4504162 0.3251202 0.3949501
0.1804663 0.3399479 0.2212054 0.3085179
0.1899286 0.3206453 0.1264308 0.1618857
, , Lag_10
0.15180484 0.1875179 -0.03011971 -0.01144414
0.05629723 0.1683812 -0.02502021 0.03307518
-0.08409547 0.1247324 -0.20909008 -0.16511840
-0.07978351 0.1421987 -0.13790670 -0.11767234
, , Lag_11
-0.11875727 0.136673414 -0.12715441 -0.1241331
-0.13047205 0.071926993 -0.12785582 -0.1132037
-0.08908832 -0.009374151 -0.14856401 -0.1563577
-0.04172053 0.093328912 0.04497078 -0.1002592
, , Lag_12
-0.0005362661 0.055885198 -0.06454129 -0.12617478
-0.0837065154 -0.030487010 -0.12492654 -0.05076665
-0.1603845066 -0.092526648 -0.18793024 -0.07192433
-0.0329581327 -0.002391903 -0.07344575 0.04800647
, , Lag_13
0.110795032 0.08497155 -0.10692939 0.039275700
0.067135202 0.08472719 -0.08162884 -0.013675882
0.003970013 0.07475564 -0.12919526 -0.199029101
0.117818973 0.25836952 0.01765533 0.005315572
, , Lag_14
0.2569690 0.4167017 0.1750001 0.099989082
0.2811720 0.3627738 0.2535473 0.142481793
0.2898364 0.3371195 0.1645728 0.008941963
0.2990899 0.3708633 0.1952323 0.025439383
, , Lag_15
0.3366081 0.3613924 0.3104424 0.1448003
0.4606357 0.4791444 0.4525622 0.2297306
0.3597465 0.3331262 0.3165522 0.1907237
0.3781567 0.3046630 0.2260963 0.2302541
, , Lag_16
0.3386886 0.2301517 0.1967166 0.2457013
0.4153102 0.2978708 0.3316045 0.3155847
0.3095268 0.2480752 0.2709355 0.3258925
0.2947141 0.2232702 0.2175240 0.3021335
, , Lag_17
0.3556765 0.2901412 0.2181435 0.3035475
0.3473894 0.3594809 0.3021085 0.3914716
0.3700382 0.4697207 0.3183025 0.4731177
0.2458445 0.4474799 0.2438784 0.4462941
Slot n:
[1] 240
Slot varnames:
[1] "Series 1" "Series 2" "Series 3" "Series 4"
Slot objectname: x
>
> ##It is an error to have more columns than rows.
> ## autocovariances(mx, maxlag = 6, nseasons = 4)
> ## autocovariances(mx)
>
> num2pcpar(mx, c(1,1,1,1), period = 4)
$mean
[1] 0.2212892 0.2610529 0.2222639 0.1751942
$coef
An object of class "slMatrix"
Slot "m":
lag
season 0 1
1 1 -0.9462426
2 1 -0.8957722
3 1 -0.8421822
4 1 -0.8803319
$scale
[1] 0.9800724 0.9837131 1.0433693 0.9435026
> num2pcpar(mx, c(3,3,3,3), period = 4)
$mean
[1] 0.2212892 0.2610529 0.2222639 0.1751942
$coef
An object of class "slMatrix"
Slot "m":
lag
season 0 1 2 3
1 1 -0.9962631 0.009726434 0.05118143
2 1 -0.9003005 0.144883523 -0.15588601
3 1 -0.7223822 -0.177308198 0.05138492
4 1 -0.9444632 0.052370207 0.01986882
$scale
[1] 0.9768186 0.9723284 1.0339197 0.9404123
>
> sipfm1 <- new("SiPeriodicArModel", iorder = 1, siorder = 1, pcmodel = pfm1)
> sipfm1
An object of class "SiPeriodicArModel"
iorder: 1, siorder: 1
PAR part of the model, An object of class "PeriodicArModel"
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1 1 1 1
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 1 5 9
S2 2 6 10
S3 3 7 11
S4 4 8 12
> fitPM(sipfm1, mx)
An object of class "SiPeriodicArModel"
iorder: 1, siorder: 1
PAR part of the model, An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1.900178 2.014823 2.512701 1.919159
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.03633172 0.02831852 0.02492281
S2 -0.02263934 -0.21176922 0.09351833
S3 -0.17347841 0.01862405 0.13451737
S4 0.02770379 0.11071311 0.07843985
number of obs. for each season:
238 238 238 238
> pfm1
An object of class "PeriodicArModel"
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1 1 1 1
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 1 5 9
S2 2 6 10
S3 3 7 11
S4 4 8 12
>
>
> ## experiments and testing
> fit1 <- fitPM(c(3,3,3,3), simts1)
> fit1_mf <- new("MultiFilter", coef = fit1@ar@coef)
> vs <- mcompanion::mf_VSform(fit1_mf, form = "I")
> tmp <- mcompanion::VAR2pcfilter(vs$Phi[ , -4],
+ Phi0inv = vs$Phi0inv, D = fit1@sigma2, what = "")
> names(tmp) # "pcfilter" "var" "Uform"
[1] "pcfilter" "var" "Uform"
> tmp$var
[1] 0.9074826 0.9903078 0.9714497 1.0608910
> zapsmall(tmp$pcfilter)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.11396728 -0.12246918 0.00759003 0 0 0
[2,] -0.00450794 0.08337720 0.01641127 0 0 0
[3,] -0.04491259 -0.00527692 0.03962269 0 0 0
[4,] 0.03387766 -0.07038516 0.00013228 0 0 0
> fit1@ar@coef
lag
season 1 2 3
1 0.11396728 -0.122469184 0.0075900305
2 -0.00450794 0.083377197 0.0164112748
3 -0.04491259 -0.005276924 0.0396226884
4 0.03387766 -0.070385164 0.0001322766
> all.equal(tmp$pcfilter[ , 1:3], fit1@ar@coef, check.attributes = FALSE) # TRUE
[1] TRUE
> tmp$Uform
$Sigma
[1] 1.0608910 0.9714497 0.9903078 0.9074826
$U0
[,1] [,2] [,3] [,4]
[1,] 1 -0.03387766 0.07038516 -0.0001322766
[2,] 0 1.00000000 0.04491259 0.0052769241
[3,] 0 0.00000000 1.00000000 0.0045079396
[4,] 0 0.00000000 0.00000000 1.0000000000
$U
[,1] [,2] [,3]
[1,] 1.224810e-18 7.453890e-20 -2.117582e-22
[2,] 3.962269e-02 0.000000e+00 -6.776264e-21
[3,] 8.337720e-02 1.641127e-02 0.000000e+00
[4,] 1.139673e-01 -1.224692e-01 7.590031e-03
$U0inv
[,1] [,2] [,3] [,4]
[1,] 1 0.03387766 -0.07190670 0.0002776578
[2,] 0 1.00000000 -0.04491259 -0.0050744608
[3,] 0 0.00000000 1.00000000 -0.0045079396
[4,] 0 0.00000000 0.00000000 1.0000000000
$perm
[1] 4 3 2 1
> fit1@sigma2
[1] 1.0608910 0.9714497 0.9903078 0.9074826
>
> ## both give the matrix Sigma for the "I" form
> identical(
+ vs$Phi0inv %*% diag(fit1@sigma2) %*% t(vs$Phi0inv)
+ ,
+ tmp$Uform$U0inv %*% diag(tmp$Uform$Sigma) %*% t(tmp$Uform$U0inv)
+ ) # TRUE
[1] TRUE
>
> ## no, this is a different matrix
> var1_mat <- cbind(vs$Phi0, # identity matrix
+ - vs$Phi) # drop trailing zero columns?
> var1_mat <- mcompanion::mCompanion(var1_mat)
> var1_Sigma <- vs$Phi0inv %*% diag(fit1@sigma2) %*% t(vs$Phi0inv)
> abs(eigen(diag(nrow(var1_mat)) - var1_mat)$values)
Error in getClassDef(x@superClass, package = packageSlot(x))@virtual :
no applicable method for `@` applied to an object of class "NULL"
Calls: eigen ... .selectSuperClasses -> vapply -> FUN -> isVirtualExt
Execution halted
Flavor: r-release-macos-x86_64
Version: 0.15.7
Check: examples
Result: ERROR
Running examples in ‘pcts-Ex.R’ failed
The error most likely occurred in:
> ### Name: fitPM
> ### Title: Fit periodic time series models
> ### Aliases: fitPM fitPM-methods fitPM,ANY,ANY-method
> ### fitPM,mcSpec,ANY-method fitPM,numeric,ANY-method
> ### fitPM,PeriodicArModel,ANY-method
> ### fitPM,PeriodicArModel,PeriodicMTS-method
> ### fitPM,PeriodicArModel,PeriodicTS-method
> ### fitPM,PiPeriodicArModel,ANY-method fitPM,SiPeriodicArModel,ANY-method
> ### Keywords: pcts methods
>
> ### ** Examples
>
> ## newm1 <- list(phi = matrix(1:12, nrow=4), p=rep(3,4), period=4, si2 = rep(1,4))
> ## new_pfm1 <- PeriodicFilterModel(newm1, intercept=0)
>
> ## generate some data;
> set.seed(1234)
> simts1 <- pcts(rnorm(1024), nseasons = 4)
>
> fitPM(c(3,3,3,3), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
> fitPM(3, simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
> ## the fit on the underlying data is equivalent.
> fitPM(c(3,3,3,3), as.numeric(simts1))
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
>
> ## equivalently, use a PAR(3,3,3,3) model for argument 'model'
> ## here the coefficients of pfm1 are ignored, since the estimation is linear.
> pfm1 <- PeriodicArModel(matrix(1:12, nrow = 4), order = rep(3,4), sigma2 = 1)
> pfm1
An object of class "PeriodicArModel"
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1 1 1 1
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 1 5 9
S2 2 6 10
S3 3 7 11
S4 4 8 12
> ## these give same results as above
> fitPM(pfm1, simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
> fitPM(pfm1, as.numeric(simts1))
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9714497 0.9903078 0.9074826
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.11396728 -0.122469184 0.0075900305
S2 -0.00450794 0.083377197 0.0164112748
S3 -0.04491259 -0.005276924 0.0396226884
S4 0.03387766 -0.070385164 0.0001322766
number of obs. for each season:
256 256 256 256
>
> fitPM(c(1,1,1,1), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.075896 0.978132 0.9917217 0.91232
Periodic order: AR(1,1,1,1)
ar1
S1 0.108486672
S2 0.001318166
S3 -0.041828251
S4 0.036776157
number of obs. for each season:
256 256 256 256
> fitPM(c(3,2,2,1), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060947 0.9717132 0.9917187 0.91232
Periodic order: AR(3,2,2,1)
ar1 ar2 ar3
S1 0.113395504 -0.122778518 -0.0004315542
S2 -0.006365449 0.084234949 NA
S3 -0.041825811 -0.001666751 NA
S4 0.036776157 NA NA
number of obs. for each season:
256 256 256 256
> fitPM(c(3,2,2,2), simts1)
An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: -0.06076922 -0.002750496 -0.0114049 -0.05637432
SigmaSq: 1.060891 0.9717132 0.9917187 0.9074826
Periodic order: AR(3,2,2,2)
ar1 ar2 ar3
S1 0.113967280 -0.122469184 0.007590031
S2 -0.006365449 0.084234949 NA
S3 -0.041825811 -0.001666751 NA
S4 0.033877417 -0.070384980 NA
number of obs. for each season:
256 256 256 256
>
> pdSafeParOrder(c(3,2,2,1))
[1] 3 2 2 2
> pdSafeParOrder(rev(c(3,2,2,1)))
[1] 1 2 2 3
>
> x <- arima.sim(list(ar = 0.9), n = 960)
> pcx <- pcts(x, nseasons = 4)
> mx <- matrix(x, nrow = 4)
>
> ##pc.acf(mx)
> ##pc.acf(mx, maxlag=10)
> ## TODO: avoid the warning when length ot the time series is not multiple
> autocovariances(t(mx), maxlag = 6, nseasons = 4)
An object of class "Lagged2d"
Slot *data*:
Lag_0 Lag_1 Lag_2 Lag_3 Lag_4 Lag_5 Lag_6
4.413696 3.698001 3.444325 3.083434 2.981296 2.740127 2.443547
5.256342 4.480398 3.791234 3.376241 2.959491 2.945734 2.704898
4.544553 4.226788 3.573730 2.787481 2.179323 1.972540 2.118425
4.744290 4.149401 3.620436 3.039397 2.267672 1.595720 1.419777
6.188442 4.913592 4.132992 3.413550 2.893992 1.944995 1.202820
5.079685 5.095852 4.020852 3.462512 2.909262 2.517747 1.724930
4.452634 4.097511 4.466851 3.581613 2.929054 2.568013 2.159905
4.028403 3.766262 3.375314 3.895400 3.027806 2.462040 2.266185
4.256483 3.789805 3.519559 3.181409 3.595804 2.723855 2.312791
5.064014 4.082757 3.780803 3.857862 3.271179 3.849951 2.962659
4.610889 4.241189 3.677709 3.378335 3.220566 2.679142 3.078473
4.492845 4.083564 3.715123 3.132441 2.782178 2.780713 2.430421
4.759835 4.221811 3.785254 3.334577 2.753388 2.379137 2.523684
4.176337 3.914353 3.379820 3.318057 2.895464 2.089529 1.813878
4.256341 3.795704 3.522276 3.120697 3.030260 2.548401 1.864357
4.123065 3.730340 3.387307 3.057720 2.614952 2.406073 2.003338
> autocovariances(t(mx))
An object of class "SampleAutocovariances"
, , Lag_0
4.940244 4.425333 3.856613 3.312517
4.425333 4.931782 4.153459 3.576585
3.856613 4.153459 4.586589 4.037720
3.312517 3.576585 4.037720 4.444731
, , Lag_1
3.053109 3.270405 3.765441 4.205794
2.830618 3.009076 3.520014 3.771935
2.262606 2.432067 2.839903 3.242108
1.928944 2.007498 2.285101 2.672126
, , Lag_2
1.754047 1.767143 2.068452 2.409498
1.430174 1.496301 1.828329 2.244051
1.298815 1.204518 1.458011 1.818073
1.238070 1.181317 1.385222 1.602964
, , Lag_3
1.3489948 1.2683819 1.4101905 1.573585
0.9844681 0.8640092 1.0299877 1.269531
0.9647423 0.9969533 0.9680465 1.158972
1.1158544 1.1012862 1.0621654 1.144140
, , Lag_4
1.0681173 1.1595254 1.0799309 1.1913186
0.8013338 0.9328923 0.7987529 0.8834352
0.8382119 0.9644516 0.8820007 0.8776038
0.9012098 1.0414786 1.0527977 0.9306610
, , Lag_5
0.8764845 1.0569533 1.0166630 0.9120474
0.8499661 1.0786949 0.8387545 0.7325839
0.6867834 0.9726713 0.8366954 0.8057214
0.8406734 1.0936087 0.8225248 0.8450239
, , Lag_6
0.9444448 1.1035309 0.8113775 0.8291835
1.1822682 1.2821839 1.0012574 0.8592283
0.8407831 0.8361079 0.6282596 0.6109829
0.8258035 0.7665833 0.6435341 0.6931841
, , Lag_7
0.8689502 0.7994249 0.7699847 0.7690557
1.0024370 0.9676477 0.9242707 0.9797624
0.6284261 0.5784530 0.5320890 0.5642380
0.5484201 0.5434399 0.4715212 0.5167750
, , Lag_8
0.3468684 0.4347957 0.4606310 0.4804946
0.6027137 0.5625705 0.6254156 0.6782344
0.4822539 0.4593642 0.4413930 0.4271425
0.4255004 0.4902419 0.4739370 0.3877285
, , Lag_9
0.2562200 0.3388748 0.2662562 0.2117790
0.3822483 0.4504162 0.3251202 0.3949501
0.1804663 0.3399479 0.2212054 0.3085179
0.1899286 0.3206453 0.1264308 0.1618857
, , Lag_10
0.15180484 0.1875179 -0.03011971 -0.01144414
0.05629723 0.1683812 -0.02502021 0.03307518
-0.08409547 0.1247324 -0.20909008 -0.16511840
-0.07978351 0.1421987 -0.13790670 -0.11767234
, , Lag_11
-0.11875727 0.136673414 -0.12715441 -0.1241331
-0.13047205 0.071926993 -0.12785582 -0.1132037
-0.08908832 -0.009374151 -0.14856401 -0.1563577
-0.04172053 0.093328912 0.04497078 -0.1002592
, , Lag_12
-0.0005362661 0.055885198 -0.06454129 -0.12617478
-0.0837065154 -0.030487010 -0.12492654 -0.05076665
-0.1603845066 -0.092526648 -0.18793024 -0.07192433
-0.0329581327 -0.002391903 -0.07344575 0.04800647
, , Lag_13
0.110795032 0.08497155 -0.10692939 0.039275700
0.067135202 0.08472719 -0.08162884 -0.013675882
0.003970013 0.07475564 -0.12919526 -0.199029101
0.117818973 0.25836952 0.01765533 0.005315572
, , Lag_14
0.2569690 0.4167017 0.1750001 0.099989082
0.2811720 0.3627738 0.2535473 0.142481793
0.2898364 0.3371195 0.1645728 0.008941963
0.2990899 0.3708633 0.1952323 0.025439383
, , Lag_15
0.3366081 0.3613924 0.3104424 0.1448003
0.4606357 0.4791444 0.4525622 0.2297306
0.3597465 0.3331262 0.3165522 0.1907237
0.3781567 0.3046630 0.2260963 0.2302541
, , Lag_16
0.3386886 0.2301517 0.1967166 0.2457013
0.4153102 0.2978708 0.3316045 0.3155847
0.3095268 0.2480752 0.2709355 0.3258925
0.2947141 0.2232702 0.2175240 0.3021335
, , Lag_17
0.3556765 0.2901412 0.2181435 0.3035475
0.3473894 0.3594809 0.3021085 0.3914716
0.3700382 0.4697207 0.3183025 0.4731177
0.2458445 0.4474799 0.2438784 0.4462941
Slot n:
[1] 240
Slot varnames:
[1] "Series 1" "Series 2" "Series 3" "Series 4"
Slot objectname: x
>
> ##It is an error to have more columns than rows.
> ## autocovariances(mx, maxlag = 6, nseasons = 4)
> ## autocovariances(mx)
>
> num2pcpar(mx, c(1,1,1,1), period = 4)
$mean
[1] 0.2212892 0.2610529 0.2222639 0.1751942
$coef
An object of class "slMatrix"
Slot "m":
lag
season 0 1
1 1 -0.9462426
2 1 -0.8957722
3 1 -0.8421822
4 1 -0.8803319
$scale
[1] 0.9800724 0.9837131 1.0433693 0.9435026
> num2pcpar(mx, c(3,3,3,3), period = 4)
$mean
[1] 0.2212892 0.2610529 0.2222639 0.1751942
$coef
An object of class "slMatrix"
Slot "m":
lag
season 0 1 2 3
1 1 -0.9962631 0.009726434 0.05118143
2 1 -0.9003005 0.144883523 -0.15588601
3 1 -0.7223822 -0.177308198 0.05138492
4 1 -0.9444632 0.052370207 0.01986882
$scale
[1] 0.9768186 0.9723284 1.0339197 0.9404123
>
> sipfm1 <- new("SiPeriodicArModel", iorder = 1, siorder = 1, pcmodel = pfm1)
> sipfm1
An object of class "SiPeriodicArModel"
iorder: 1, siorder: 1
PAR part of the model, An object of class "PeriodicArModel"
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1 1 1 1
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 1 5 9
S2 2 6 10
S3 3 7 11
S4 4 8 12
> fitPM(sipfm1, mx)
An object of class "SiPeriodicArModel"
iorder: 1, siorder: 1
PAR part of the model, An object of class FittedPeriodicArModel
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1.900178 2.014823 2.512701 1.919159
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 0.03633172 0.02831852 0.02492281
S2 -0.02263934 -0.21176922 0.09351833
S3 -0.17347841 0.01862405 0.13451737
S4 0.02770379 0.11071311 0.07843985
number of obs. for each season:
238 238 238 238
> pfm1
An object of class "PeriodicArModel"
Number of seasons: 4
Mean: 0 0 0 0
SigmaSq: 1 1 1 1
Periodic order: AR(3,3,3,3)
ar1 ar2 ar3
S1 1 5 9
S2 2 6 10
S3 3 7 11
S4 4 8 12
>
>
> ## experiments and testing
> fit1 <- fitPM(c(3,3,3,3), simts1)
> fit1_mf <- new("MultiFilter", coef = fit1@ar@coef)
> vs <- mcompanion::mf_VSform(fit1_mf, form = "I")
> tmp <- mcompanion::VAR2pcfilter(vs$Phi[ , -4],
+ Phi0inv = vs$Phi0inv, D = fit1@sigma2, what = "")
> names(tmp) # "pcfilter" "var" "Uform"
[1] "pcfilter" "var" "Uform"
> tmp$var
[1] 0.9074826 0.9903078 0.9714497 1.0608910
> zapsmall(tmp$pcfilter)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0.11396728 -0.12246918 0.00759003 0 0 0
[2,] -0.00450794 0.08337720 0.01641127 0 0 0
[3,] -0.04491259 -0.00527692 0.03962269 0 0 0
[4,] 0.03387766 -0.07038516 0.00013228 0 0 0
> fit1@ar@coef
lag
season 1 2 3
1 0.11396728 -0.122469184 0.0075900305
2 -0.00450794 0.083377197 0.0164112748
3 -0.04491259 -0.005276924 0.0396226884
4 0.03387766 -0.070385164 0.0001322766
> all.equal(tmp$pcfilter[ , 1:3], fit1@ar@coef, check.attributes = FALSE) # TRUE
[1] TRUE
> tmp$Uform
$Sigma
[1] 1.0608910 0.9714497 0.9903078 0.9074826
$U0
[,1] [,2] [,3] [,4]
[1,] 1 -0.03387766 0.07038516 -0.0001322766
[2,] 0 1.00000000 0.04491259 0.0052769241
[3,] 0 0.00000000 1.00000000 0.0045079396
[4,] 0 0.00000000 0.00000000 1.0000000000
$U
[,1] [,2] [,3]
[1,] -1.829718e-19 6.802028e-20 -3.263662e-22
[2,] 3.962269e-02 4.045944e-20 2.495333e-21
[3,] 8.337720e-02 1.641127e-02 -1.224348e-21
[4,] 1.139673e-01 -1.224692e-01 7.590031e-03
$U0inv
[,1] [,2] [,3] [,4]
[1,] 1 0.03387766 -0.07190670 0.0002776578
[2,] 0 1.00000000 -0.04491259 -0.0050744608
[3,] 0 0.00000000 1.00000000 -0.0045079396
[4,] 0 0.00000000 0.00000000 1.0000000000
$perm
[1] 4 3 2 1
> fit1@sigma2
[1] 1.0608910 0.9714497 0.9903078 0.9074826
>
> ## both give the matrix Sigma for the "I" form
> identical(
+ vs$Phi0inv %*% diag(fit1@sigma2) %*% t(vs$Phi0inv)
+ ,
+ tmp$Uform$U0inv %*% diag(tmp$Uform$Sigma) %*% t(tmp$Uform$U0inv)
+ ) # TRUE
[1] TRUE
>
> ## no, this is a different matrix
> var1_mat <- cbind(vs$Phi0, # identity matrix
+ - vs$Phi) # drop trailing zero columns?
> var1_mat <- mcompanion::mCompanion(var1_mat)
> var1_Sigma <- vs$Phi0inv %*% diag(fit1@sigma2) %*% t(vs$Phi0inv)
> abs(eigen(diag(nrow(var1_mat)) - var1_mat)$values)
Error in isVirtualExt(exti) :
trying to get slot "virtual" from an object of a basic class ("NULL") with no slots
Calls: eigen ... .selectSuperClasses -> vapply -> FUN -> isVirtualExt
Execution halted
Flavor: r-oldrel-macos-arm64
Version: 0.15.5
Check: package dependencies
Result: NOTE
Package suggested but not available for checking: ‘partsm’
Flavor: r-oldrel-macos-x86_64