R Under development (unstable) (2024-02-17 r85935 ucrt) -- "Unsuffered Consequences"
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> 
> #######################################################################
> 
> #    test functions for examples in the Brief User's Guide   <<<<<<<<<<
> 
> #######################################################################
> 
> 
> 
> # The tests requiring padi and dsepadi are in dsepadi/tests.
> 
> if(!require("EvalEst"))  stop("this test requires EvalEst.")
Loading required package: EvalEst
Loading required package: tfplot
Loading required package: tframe
Loading required package: dse

Attaching package: 'dse'

The following objects are masked from 'package:stats':

    acf, simulate

> if(!require("setRNG"))stop("this test requires setRNG.")
Loading required package: setRNG
>  Sys.info()
       sysname        release        version       nodename        machine 
     "Windows"   "Server x64"  "build 20348"     "CRANWIN3"       "x86-64" 
         login           user effective_user 
        "CRAN"         "CRAN"         "CRAN" 
>  DSEversion()
       setRNG        tframe           dse       EvalEst 
   "2024.2-1" "2015.12-1.1"    "2024.2-1"    "2024.2-1" 
> 
> print.values <- FALSE
> fuzz.small <- 1e-14
> fuzz.large <- 1e-8
> max.error <- NA
> all.ok <- TRUE
> 
> 
>   cat("Guide part 2 test 1 ... \n")
Guide part 2 test 1 ... 
> 
>   # Old Splus value 
>   #  {test.rng1 <- list(kind="default", normal.kind="default", 
>   #                     seed=c(13,44,1,25,56,0,6,33,22,13,13,0) )
>   #   test.rng2 <- list(kind="default", normal.kind="default", 
>   #                      seed=c(13,43,7,57,62,3,30,29,24,54,47,2) )
>   #   test.rng4 <- list(kind="default", normal.kind="default", 
>   #                     seed=c(29,55,47,18,33,1,15,15,34,46,13,2) )
>   #   test.rng3 <- list(kind="default", normal.kind="default", 
>   #                      seed=c( 53,41,26,39,10,1,19,25,56,32,28,3) )
>   #As of R 1.7.1 a bug was recognized in Kinderman-Ramage and it was changed.
>   #   These tests use the old version named "Buggy Kinderman-Ramage" to
>   #   get old results for comparison.
>   test.rng1 <- test.rng2 <- test.rng3 <- test.rng4 <- 
+   if (as.numeric(version$major)+0.1*as.numeric(version$minor) >= 1.71 )
+            list(kind="Wichmann-Hill", normal.kind="Buggy Kinderman-Ramage",
+ 	        seed=c(979, 1479, 1542)) else
+            list(kind="Wichmann-Hill", normal.kind="Kinderman-Ramage",
+ 	        seed=c(979, 1479, 1542))
> 	   # might consider also 
> 	   #list(kind="Wichmann-Hill", normal.kind="user-supplied", seed=c(979, 1479, 1542))}
>            # R's Box-Muller was declared not reproducible. 
> 	   
> 
> #eg1.DSE.data <- t(matrix(scan(paste(DSE.HOME, "/data/eg1.dat", sep="")),
> #                         5, 364))[, 2:5] 
> #eg1.DSE.data <- TSdata(input = eg1.DSE.data[,1,drop = FALSE], 
> #                      output = eg1.DSE.data[, 2:4, drop = FALSE])
> 
> # above worked, but needs DSE.HOME which is depreciated. use
> data("eg1.DSE.data", package="dse")
> 	      
> eg1.DSE.data <-tframed(eg1.DSE.data, list(start=c(1961,3), frequency=12))
> 
> seriesNamesInput(eg1.DSE.data) <- "R90"
> seriesNamesOutput(eg1.DSE.data) <- c("M1","GDPl2", "CPI")
> 
> 
> data("egJofF.1dec93.data", package="dse")
> 
> if(!exists("egJofF.1dec93.data"))warning("egJofF.1dec93.data does not exist")
> 
>   error <- abs(3352.4721630925987483 - 
+             sum(c(outputData(egJofF.1dec93.data),
+                    inputData(egJofF.1dec93.data))))
>   ok <-  fuzz.large > error
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok &ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
> # Section 4 - Model Estimation
> 
>   cat("Guide part 2 test 2 ... \n")
Guide part 2 test 2 ... 
>   model.eg1.ls <- estVARXls(trimNA(eg1.DSE.data), warn=FALSE)
> #  opts <-options(warn=-1) 
>     subsample.data <- tfwindow(eg1.DSE.data,start=c(1972,1),end=c(1992,12),warn=FALSE)
> #  options(opts)
>   # summary(model.eg1.ls)
>   # print(model.eg1.ls)
>   
>   tfplot(model.eg1.ls)
>   tfplot(model.eg1.ls, start=c(1990,1))
> 
>  
>   z <- checkResiduals(model.eg1.ls, plot.=FALSE, pac=TRUE)
>   # Old Splus value c(4.67445135116577148, 3274.42578125,      -2371.9997808950302)
>   check.value <-    c(4.674448837156188,   3274.4226534894947, -2371.999780895034)
> # above minor modification of the value in 1.8.0 beta
> #  else        c(4.674448837156188,   3274.422653488969,  -2371.999780895034) )
> # using my old acf instead of bats version gives
> #             c(4.6744488371561879,      0.0,       -2371.999780895033837)
> 
>   tst <- c(sum(z$acf),sum(z$pacf),sum(z$cusum))
>   printTestValue(c(tst), digits=18)
c( ,  4.67444883715623494,  3274.42265348895216,  -2371.99978089511342)
>   error <- max(abs(check.value   -   tst ))
> 
>   ok <-  fuzz.large > error 
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 3 ... \n")
Guide part 2 test 3 ... 
>   # NB- non-stationary data. ar is not really valid
>   model.eg1.ar <- estVARXar(trimNA(eg1.DSE.data), warn=FALSE) 
>   model.eg1.ss <- estSSfromVARX(trimNA(eg1.DSE.data), warn=FALSE) 
> # model.eg1.mle <- estMaxLik(trimNA(eg1.DSE.data),model.eg1.ar) # this may be slow
>   # Old Splus value c(6738.642280883833, 6921.352391513382)
>   check.value <-    c(6738.562190977154, 6921.352391513382)#ts ar
>   # R bats ar       c(6735.139112062216, 6921.352391513382)bats ar
> # using my old ar:=ls gives c(6921.352391513380, 6921.352391513380)#ar:=ls
> 
>   tst <- c(model.eg1.ar$estimates$like[1], model.eg1.ss$estimates$like[1])  
>   printTestValue(c(tst), digits=18)
c( ,  6738.56219093239906,  6921.35239151337919)
>   error <- max(abs(check.value - tst))
> 
>   ok <- 10*fuzz.large > error 
>   if ((Sys.info()[["sysname"]] == "Linux") && ! ok) {
+     warning("Using relaxed tolerance for Linux.")   
+     ok <- 100*fuzz.large > error
+     }   
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 4a... \n")
Guide part 2 test 4a... 
>    eg4.DSE.data<- egJofF.1dec93.data
>    outputData(eg4.DSE.data) <- outputData(eg4.DSE.data, series=c(1,2,6,7))
>  # following is optional 
>  # tframe(outputData(eg4.DSE.data))<- tframe(outputData(egJofF.1dec93.data))
> 
>   model.eg4.bb <- estBlackBox(trimNA(eg4.DSE.data), max.lag=3, verbose=FALSE) 
> 
>   tst <- model.eg4.bb$estimates$like[1]
>   printTestValue(c(tst), digits=18)
c( ,  614.705003135903439)
>   error <- abs(614.70500313590287078 - tst )
> 
>   ok <-  fuzz.large > error 
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
> 
>   cat("Guide part 2 test 4b... \n")
Guide part 2 test 4b... 
>   z <- informationTests(model.eg1.ar, model.eg1.ss, Print=FALSE, warn=FALSE)
> #  # Old Splus value   check.value <- 231152.464267979725
> #  R check.value <- 231151.0300943982  # ts ar
> #  R check.value <- 230978.2532793634  bats ar
> #  R using my old ar=ls gives        233856.9237566061   #using ls for ar
> # a small change in the accounting for degenerate subspaces in dse.2000.4 gives
>   # Splus value    225328.464267979754 
>    check.value <-  225327.03009431256
>   if (print.values) printTestValue(sum(z[!is.na(z)]) )  
>   error <- abs(check.value - sum(z[!is.na(z)]) )
>   ok <-  1000*fuzz.large > error #fuzz.large works in Solaris but not Linux
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
> 
> # Section 5 - Forecasting
>   cat("Guide part 2 test 5 pre-test a... \n")
Guide part 2 test 5 pre-test a... 
>   eg4.DSE.model <- estVARXls(eg4.DSE.data)
>   longIn.data <- TSdata(
+               input= ts(rbind(inputData(eg4.DSE.data), matrix(.1,10,1)), 
+                        start=start(eg4.DSE.data),
+                        frequency=frequency(eg4.DSE.data)),    
+               output=outputData(eg4.DSE.data))
>   seriesNames(longIn.data) <- seriesNames(eg4.DSE.data)
>   z  <- forecast(TSmodel(eg4.DSE.model), longIn.data)
>   zz <- forecast(TSmodel(eg4.DSE.model), longIn.data, compiled=FALSE)
>   error <- max(abs(z$pred - zz$pred))
>   ok <-  fuzz.small > error 
>   if (ok) cat("ok\n") else {
+      max.error <- if (is.na(max.error)) error else max(error, max.error)
+      cat("failed! error= ", error,"\n") 
+      if(!testEqual(z$data$output, zz$data$output))
+          cat("output data comparison for forecast() and longIn.data failed.\n") 
+      if(!testEqual(z$data$input, zz$data$input))
+          cat("input data comparison for forecast() and longIn.data failed.\n") 
+      }
ok
>   all.ok <- all.ok & ok 
> 
>   cat("Guide part 2 test 5 ... \n")
Guide part 2 test 5 ... 
>   eg4.DSE.model <- estVARXls(eg4.DSE.data)
> #  Fame call disabled for testing: new.data <- freeze(eg4.DSE.data.names) 
>   new.data <- TSdata(
+               input= ts(rbind(inputData(eg4.DSE.data), matrix(.1,10,1)), 
+                        start=start(eg4.DSE.data),
+                        frequency=frequency(eg4.DSE.data)),    
+               output=ts(rbind(outputData(eg4.DSE.data),matrix(.3,5,4)), 
+                        start=start(eg4.DSE.data),
+                        frequency=frequency(eg4.DSE.data)))
>   seriesNames(new.data) <- seriesNames(eg4.DSE.data)
>   z  <- l(TSmodel(eg4.DSE.model), trimNA(new.data)) 
> #  z <- l(TSmodel(eg4.DSE.model), trimNA(freeze(eg4.DSE.data.names)))
>   error <- max(abs(556.55870662521476788 -z$estimates$like[1]))
>   ok <-  fuzz.large > error 
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 6 ... \n")
Guide part 2 test 6 ... 
>   zz <- forecast(TSmodel(eg4.DSE.model), new.data)
>   #tfplot(zz$pred, forecast(TSmodel(eg4.DSE.model), new.data, compiled=FALSE)$pred) 
>   z <-  forecast(TSmodel(eg4.DSE.model), trimNA(new.data), 
+ 		conditioning.inputs=inputData(new.data))
>   tfplot(zz, start=c(1990,6))
>   error <- abs(4.7990339556773520258 - sum(forecasts(z)[[1]]))
>   ok <- fuzz.small > error
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   ok <- testEqual(zz,z) & ok
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 7 ... \n")
Guide part 2 test 7 ... 
>   z <- forecast(eg4.DSE.model, conditioning.inputs.forecasts=matrix(.5,6,1)) 
>   # Fame call disabled for testing: 
>   # z <- forecast(TSmodel(eg4.DSE.model), freeze(egJofF.1dec93.data.names), 
>   #		conditioning.inputs.forecasts=matrix(.5,6,1))
>   # summary(z)
>   # print(z)
>   tfplot(z)
>   tfplot(z, start=c(1990,1))
>   
>   #  forecasts(z)[[1]]
>   #  tfwindow(forecasts(z)[[1]], start=c(1994,5)) 
>   ok <- all(start(eg4.DSE.model$data)   == c(1974,2)) &
+         all(start(egJofF.1dec93.data) == c(1974,2)) 
>   error <- max(abs(c(5.9414711908521793404 - sum(forecasts(z)[[1]][1:6,]),
+                   3.7410224783909828972 - 
+                      sum(tfwindow(forecasts(z)[[1]], start=c(1993,12), warn=FALSE)))))
>   ok <-  ok & (fuzz.small > error) 
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 8 ... \n")
Guide part 2 test 8 ... 
>   z <- l(TSmodel(eg4.DSE.model), new.data)
>   tfplot(z)
>   z <- featherForecasts(TSmodel(eg4.DSE.model), new.data)
>   tfplot(z)
>   zz <-featherForecasts(TSmodel(eg4.DSE.model), new.data,
+                           from.periods =c(20,50,60,70,80), horizon=150)
>   tfplot(zz)
>   error <- max(abs(c(54.838475604100473504 -
+                            sum( forecasts(z)[[1]][10:46,]),
+                        53.824873541066445171 - 
+                            sum(forecasts(zz)[[5]][80:116,]))))
>   ok <-fuzz.large > error
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 9 ... \n")
Guide part 2 test 9 ... 
>   z <- horizonForecasts(TSmodel(eg4.DSE.model), new.data, horizons=c(1,3,6))
>   tfplot(z)
> #  error <- abs(653.329319170802592 - sum(z$horizonForecasts) )
>   error <- abs(653.329319170802592 - sum(forecasts(z)) )
>   ok <-  fuzz.large > error 
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 10... \n")
Guide part 2 test 10... 
>   fc1 <- forecastCov(TSmodel(eg4.DSE.model), data=eg4.DSE.data)
>   
>   tfplot(fc1)
>   tfplot(forecastCov(TSmodel(eg4.DSE.model), data=eg4.DSE.data, horizons= 1:4)) 
>  
>   fc2 <- forecastCov(TSmodel(eg4.DSE.model),
+             data=eg4.DSE.data, zero=TRUE, trend=TRUE)
>   tfplot(fc2)
>   error <- max(abs(c(14.933660144821400806 - sum(fc1$forecastCov[[1]]),
+                         14.933660144821400806 - sum(fc2$forecastCov[[1]]),
+                         31.654672476928297442 - sum(fc2$forecastCov.zero),
+                         18.324461923341953451 - sum(fc2$forecastCov.trend) )))
>   ok <- (fuzz.small * 1.5) > error
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   if (is.na(ok)) ok <- FALSE
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 11... \n")
Guide part 2 test 11... 
>   mod1 <- ARMA(A=array(c(1,-.25,-.05), c(3,1,1)), B=array(1,c(1,1,1)))
>   mod2 <- ARMA(A=array(c(1,-.8, -.2 ), c(3,1,1)), B=array(1,c(1,1,1)))
>   mod3 <- ARMA(
+  	A=array(c( 
+  	1.00,-0.06,0.15,-0.03,0.00,0.02,0.03,-0.02,0.00,-0.02,-0.03,-0.02,
+ 	0.00,-0.07,-0.05,0.12,1.00,0.20,-0.03,-0.11,0.00,-0.07,-0.03,0.08,
+  	0.00,-0.40,-0.05,-0.66,0.00,0.00,0.17,-0.18,1.00,-0.11,-0.24,-0.09 )
+ 		,c(4,3,3)), 
+  	B=array(diag(1,3),c(1,3,3)))
>   e.ls.mod1 <- EstEval( mod1, replications=100, 
+  	rng=test.rng1,
+  	simulation.args=list(sampleT=100, sd=1), 
+  	estimation="estVARXls", estimation.args=list(max.lag=2), 
+  	criterion="TSmodel", quiet=TRUE)
Warning message:
In RNGkind(kind = kind, normal.kind = normal.kind) :
  buggy version of Kinderman-Ramage generator used
> 
> #    e.ar.mod1 <- EstEval( mod1, replications=100, 
> #   	rng=test.rng1,
> #   	simulation.args=list(sampleT=100, sd=1), 
> #   	estimation="estVARXar", estimation.args=list(max.lag=2, aic=FALSE), 
> #   	criterion="TSmodel", quiet=TRUE)
> #   tfplot(coef(e.ar.mod1))
> 
> 
>   # Old Splus value -0.29855874505752699744 
>   check.value <-    -0.3699580622977686
>   error <- abs( check.value - sum(coef(e.ls.mod1$result[[100]])))
>   ok <- fuzz.small > error
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 12... \n")
Guide part 2 test 12... 
>   e.ls.mod2 <- EstEval( mod2, replications=100, 
+                      rng=test.rng2,
+                      simulation.args=list(sampleT=100, sd=1), 
+                      estimation="estVARXls", estimation.args=list(max.lag=2), 
+                      criterion="TSmodel", quiet=TRUE)
Warning message:
In RNGkind(kind = kind, normal.kind = normal.kind) :
  buggy version of Kinderman-Ramage generator used
>      old.par <- par(mfcol=c(2,1)) #set the number of plots on the plotics device
>      on.exit(par(old.par))
>      tfplot(coef(e.ls.mod1))
>      tfplot(coef(e.ls.mod2)) 
>      old.par <- c(old.par, par(mfcol=c(2,1)) )
>      tfplot(coef(e.ls.mod1), cum=FALSE, bounds=FALSE) 
>      tfplot(coef(e.ls.mod2), cum=FALSE, bounds=FALSE) 
>      distribution(coef(e.ls.mod1), bandwidth=.2)
>      distribution(coef(e.ls.mod2), bandwidth=.2)
>     
> 
>   # Old Splus value -1.0021490287427212706 
>   check.value  <-   -1.0028944627996934
>   error <- abs(check.value - sum(coef(e.ls.mod2$result[[100]])))
>   ok <- fuzz.small > error
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 13... \n")
Guide part 2 test 13... 
>   e.ls.mod1.roots <- roots(e.ls.mod1)
>   
>      plot(e.ls.mod1.roots) 
>      plot(e.ls.mod1.roots, complex.plane=FALSE)
>      plot(roots(e.ls.mod2), complex.plane=FALSE) 
>      distribution(e.ls.mod1.roots, bandwidth=.2) 
>      distribution(roots(e.ls.mod2), bandwidth=.1) 
>   
> 
>   # Old Splus value 0.36159459310761993267 
>   check.value <-    0.2119677206564640
> # error <- Mod(0.36159459310761993267+0i - sum(e.ls.mod1.roots$result[[100]]))
>   error <- Mod(check.value   - sum(e.ls.mod1.roots$result[[100]]))
>   ok <- fuzz.small > error
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 14... \n")
Guide part 2 test 14... 
>   pc <- forecastCovEstimatorsWRTtrue(mod3,
+  	rng=test.rng3,
+  	estimation.methods=list(estVARXls=list(max.lag=6)),
+  	est.replications=2, pred.replications=10, quiet=TRUE)
Warning message:
In RNGkind(kind = kind, normal.kind = normal.kind) :
  buggy version of Kinderman-Ramage generator used
>   # the fuzz.small has to be relaxed here to accomodate differences in rnorm
>   #   between Splus3.1 and Splus3.2  (the numbers are from Splus3.2)
> 
>   # Old Splus value c(60.927013860328429473, 62.32729288591478678, 63.17808145947956433)
>   check.value <-    c(54.164759056117504,    53.519297277839669,   59.341526159411558)
>   error <- max(abs(check.value -c(sum(pc$forecastCov[[1]]), 
+                       sum(pc$forecastCov.zero), sum(pc$forecastCov.trend) )))
>   ok <- fuzz.large > error
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 15... \n")
Guide part 2 test 15... 
>   pc.rd <- forecastCovReductionsWRTtrue(mod3,
+  	rng=test.rng4,
+  	estimation.methods=list(estVARXls=list(max.lag=3)),
+  	est.replications=2, pred.replications=10, quiet=TRUE)
Warning message:
In RNGkind(kind = kind, normal.kind = normal.kind) :
  buggy version of Kinderman-Ramage generator used
> 
>   # Old Splus value c(58.75543799264762157,60.451513998215133938, 64.089618782185240775)
>   check.value <-    c(51.237201863944890,  53.519297277839669,    59.341526159411558)
>   error <- max(abs(check.value - c(sum(pc.rd$forecastCov[[1]]),
+                   sum(pc.rd$forecastCov.zero), sum(pc.rd$forecastCov.trend))))
>   ok <- fuzz.large > error
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("Guide part 2 test 16... \n")
Guide part 2 test 16... 
>   z <-outOfSample.forecastCovEstimatorsWRTdata(trimNA(eg1.DSE.data),
+  	estimation.sample=.5,
+  	estimation.methods = list(
+  		estVARXar=list(warn=FALSE), 
+  		estVARXls=list(warn=FALSE)), 
+  	trend=TRUE, zero=TRUE)
>   tfplot(z)
>   opts <- options(warn=-1)
>   zz <-outOfSample.forecastCovEstimatorsWRTdata(trimNA(eg1.DSE.data),
+  	estimation.sample=.5,
+  	estimation.methods = list(
+  		estBlackBox4=list(max.lag=3, verbose=FALSE, warn=FALSE),
+ 		estVARXls=list(max.lag=3, warn=FALSE)), 
+ 	trend=TRUE, zero=TRUE)
> 
> #    zf<-horizonForecasts(zz$multi.model[[1]],zz$data, horizons=c(1,3,6))
>     zf<-horizonForecasts(TSmodel(zz, select=1),TSdata(zz), horizons=c(1,3,6))
>   options(opts)
>   zf<- zf$horizonForecasts[3,30,]
>   tfplot(z)
>   # Old Splus value 
>   #   c(6120.97621905043979, 175568.040899036743, 24.568074094041549,
>   #     1e-10*c(158049871127.845642, 3330592793.50789356, 
>   #            1242727188.69001055, 1606263575.00784183)) 
>   check.value <-    c(6120.97621905044,  175568.0408990367,  24.56807409404155, 
+        1e-10*c(158034797997.4372,  3330592793.507894,
+               1242727188.690011,  1606263575.007842))
> # using my old ls for ar instead of bats version gives
> #    c(6120.9762190509673, 175568.04089903546, 24.568074093999403,
> #       1e-10*c(3330592793.507894, 3330592793.507894,
> #              1242727188.690011, 1606263575.0078418)) # using ls for ar
>    
> 
>   if (print.values) printTestValue(c(zf,  # 1e-5*
+                      c(sum( z$forecastCov[[1]]), sum( z$forecastCov[[2]]),
+                        sum(zz$forecastCov[[1]]), sum(zz$forecastCov[[2]]))) )  
>   error <- max(abs(check.value -
+          c(zf,  1e-10*c(sum( z$forecastCov[[1]]), sum( z$forecastCov[[2]]),
+                        sum(zz$forecastCov[[1]]), sum(zz$forecastCov[[2]])))))
>   ok <-  fuzz.large > error 
>   if (!ok) {if (is.na(max.error)) max.error <- error
+             else max.error <- max(error, max.error)}
>   all.ok <- all.ok & ok 
>   {if (ok) cat("ok\n") else cat("failed! error= ", error,"\n") }
ok
> 
>   cat("All Brief User Guide example tests part 2 completed")
All Brief User Guide example tests part 2 completed>      if (all.ok) cat(" OK\n")  else 
+         cat(", some FAILED! max.error = ", max.error,"\n")
 OK
> 
>   if (!all.ok) stop("Some tests FAILED")
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   2.03    0.17    2.18