Running 'test_cif.R' [156s]
  Running 'testthat.R' [156s]
Running the tests in 'tests/test_cif.R' failed.
Complete output:
  > library(cif)
  > 
  > #Load data
  > qtr2<-iculomb
  > ssize <-nrow(qtr2)
  > cal <- seq(from=as.Date("2020-02-24"), by="days",length.out = ssize)
  > 
  > ic = log(qtr2$terapia_intensiva)
  > hws=log(qtr2$ricoverati_con_sintomi)
  > 
  > y <- cbind(ic,hws)
  > colnames(y) <- make.names(c("ic","hws"))
  > ssize <-nrow(qtr2)
  > cal <- seq(from=as.Date("2020-02-24"), by="days",length.out = ssize)
  > 
  > n<- nrow(y);  nlag=4; nhstar=0; booseas <- F;
  > # pntdates <-c(75,76,101);
  > pntdates <-NA; drop1<-NA; drop2<-NA;
  > npl<-c(nrow(y), ncol(y), nlag)           # collected n, p, nlag
  > breaks <- NA
  > 
  > ndet=c(2,1); begtrim=7;  endtrim=7;  nforecast=7; npred=0;
  > befpn<-c(begtrim,endtrim,nforecast,npred,nhstar) # collected specs for endtrim etc
  > 
  > longcal<-seq(from=cal[1], by="days",length.out = n+npred) # total calendar
  > # boosop  <- booseas||anyNA(pntdates)
  > det1 <- ec.datadet1(n,befpn,breaks)
  > det2 <- ec.datadet2(det1,booseas,pntdates)
  > ymat <- ec.datalag(y,nlag)
  > # odates<-ec.bfind(det1,ymat,npl,befpn,ndet)
  > oua1<-ec.main(y=y,ndet=ndet,nlag,befpn,breaks,booseas,
  +               pntdates,drop1=NA,drop2=NA,cal=cal)
  > 
  > class(oua1)
  [1] "cif"
  > 
  > summary(oua1)
  	
   Cointegrating Equation:
        EG1st eq. ic_1
  hws_1         0.9888
  const        -2.3754
  trend         0.0008
  	
   Results for Equation 1 :
         Estimate Std. Error T-Value P-Value Stars
  ecm_1   -0.0089     0.0122 -0.7263  0.4676      
  Dic_1   -0.0152     0.0569 -0.2666  0.7898      
  Dhws_1   0.2014     0.0931  2.1648  0.0304    **
  Dic_2    0.0292     0.0552  0.5296  0.5964      
  Dhws_2   0.1992      0.083  2.4002  0.0164    **
  Dic_3    0.1857     0.0552  3.3654   8e-04   ***
  Dhws_3   0.0953     0.0875  1.0885  0.2764      
  const    -0.001     0.0041 -0.2477  0.8043      
  	
   Results for Equation 2 :
         Estimate Std. Error T-Value P-Value Stars
  ecm_1     0.018      0.007  2.5561  0.0106    **
  Dic_1    0.0499     0.0328  1.5209  0.1283      
  Dhws_1   0.2027     0.0537  3.7772   2e-04   ***
  Dic_2    0.0677     0.0318  2.1293  0.0332    **
  Dhws_2   0.3678     0.0479  7.6849       0   ***
  Dic_3    0.0227     0.0318  0.7148  0.4747      
  Dhws_3   0.1303     0.0505  2.5814  0.0098   ***
  const    -2e-04     0.0024 -0.0843  0.9328      
  	
   Joint significance of predictors:
         Wald stat p value Stars
  ecm_1       7.51    0.02    **
  Dic_1       2.49    0.29      
  Dhws_1     17.49       0   ***
  Dic_2       4.63     0.1     *
  Dhws_2     61.74       0   ***
  Dic_3      11.47       0   ***
  Dhws_3      7.36    0.03    **
  const       0.06    0.97      
  	
   Indices of forecast accuracy in equation 1:
          eq1:model  eq1:RW eq1:ratio eq2:model   eq2:RW eq2:ratio
  MAE        0.0284  0.0220    1.2922    0.0138   0.0477    0.2887
  MAEexp    13.3521 10.2857    1.2981   48.1008 164.7143    0.2920
  sMAPE      0.0046  0.0036    1.2912    0.0017   0.0059    0.2883
  MAPE       0.0046  0.0036    1.2981    0.0017   0.0059    0.2871
  MAPEexp    0.0289  0.0217    1.3353    0.0137   0.0493    0.2789
  MAAPE      0.0046  0.0036    1.2981    0.0017   0.0059    0.2871
  BAASE      1.2860  1.0000    1.2922    0.6222   1.0000    0.2887
  MASE       0.5267  0.4076    1.2922    0.2835   0.9821    0.2887
  AISc       0.6240  0.6501    0.9599    0.4979   0.5554    0.8966
  AISg       0.6240  0.6501    0.9599    0.4979   0.5554    0.8966
  MSISc     11.5791 12.0627    0.9599   10.2494  11.4319    0.8966
  MSISg     11.5791 12.0627    0.9599   10.2494  11.4319    0.8966
  BASISc     0.9359  1.0000    0.9359    0.8424   1.0000    0.8424
  BASISg     0.9359  1.0000    0.9359    0.8424   1.0000    0.8424
               Length Class  Mode     
  ymat         3240   -none- numeric  
  breaks          1   -none- logical  
  det1          648   -none- numeric  
  det2            1   -none- logical  
  ndet            2   -none- numeric  
  det1names       2   -none- character
  drop1           1   -none- logical  
  drop2           1   -none- logical  
  estsample     310   -none- numeric  
  npl             3   -none- numeric  
  befpn           5   -none- numeric  
  beta            2   -none- numeric  
  betad           2   -none- numeric  
  vEG1step        3   -none- numeric  
  afEG          620   -none- numeric  
  longafEG      648   -none- numeric  
  ecm           324   -none- numeric  
  alpha           2   -none- numeric  
  mPhi           12   -none- numeric  
  mPi             4   -none- numeric  
  acoef          16   -none- numeric  
  acoef_se       16   -none- numeric  
  acoef_t        16   -none- numeric  
  acoef_pval     16   -none- numeric  
  res           620   -none- numeric  
  DafEG2       1240   -none- numeric  
  Omega           4   -none- numeric  
  nu              1   -none- logical  
  zeta            4   -none- numeric  
  xi              4   -none- numeric  
  wald           16   -none- numeric  
  rwsigma         2   -none- numeric  
  rwabsmean       2   -none- numeric  
  EG1            11   -none- list     
  EG2            11   -none- list     
  afl          1944   -none- numeric  
  afd          1944   -none- numeric  
  longafl      1944   -none- numeric  
  longafd      1944   -none- numeric  
  affore         48   -none- numeric  
  longaffore   1944   -none- numeric  
  afdl           48   -none- numeric  
  afdd           48   -none- numeric  
  longafdl     1944   -none- numeric  
  longafdd     1944   -none- numeric  
  fit          1296   -none- numeric  
  dynpred        64   -none- numeric  
  mAt            64   -none- numeric  
  mB             32   -none- numeric  
  Sigmah        144   -none- numeric  
  forstartdate    1   Date   numeric  
  outcal          8   Date   numeric  
  h1star          1   Date   numeric  
  cspred          9   -none- numeric  
  tablefap       84   -none- numeric  
  t0              1   -none- numeric  
  cal           324   Date   numeric  
  > 
  > 
  > 
  > ix <- t(as.matrix(cbind(oua1$tablefap[c(1,3,4,6,9,10,13,14),3])))
  > 
  > 
  > barplot(ix,
  +         main = "Performance of the VEC model relative to the Random Walk",
  +         xlab = "",
  +         col = c("red","green","blue"),
  +         beside = TRUE
  + )
  > abline(h=1, col="blue", lty=2, lwd=1)
  > 
  > 
  > ix <- t(as.matrix(cbind(oua1$tablefap[c(1,3,4,6,9,10,13,14),3])))
  > 
  > 
  > rownames(ix)<-c("Model1")
  > 
  > myylim<-c(min(ix),max(ix)); yvec<-c(0.1,0.3,1,3,10,30)
  > plot(y=ix,x=seq(1:8),type="p",ylim=myylim,main="VEC model compared to the Random Walk",font.main=1,pch=19,col="blue",ylab="",xlab="",xaxt="n", yaxt="n");
  > 
  > xtick<-seq(1:8)
  > xticklab<-colnames(ix)
  > text(x=xtick, par("usr")[3],
  +      labels = xticklab, srt = 0, pos = 1, offset=1, xpd = TRUE)
  > 
  > ytick<- round(seq(min(ix),max(ix),length.out = 5),digits = 2)
  > yticklab<-round(seq(min(ix),max(ix),length.out = 5),digits = 2)
  > axis(side=2, at=ytick, labels = FALSE)
  > text(par("usr")[1], y=ytick,
  +      labels = yticklab, srt = 0, pos = 2, xpd = TRUE)
  > 
  > ytick1<- 1
  > yticklab1<-1
  > axis(side=2, at=ytick1, labels = FALSE)
  > text(par("usr")[1], y=ytick1,
  +      labels = yticklab1, srt = 0, pos = 2, xpd = TRUE)
  > 
  > 
  > abline(h=1, col="black", lty=2, lwd=1)
  > 
  > 
  > 
  > 
  > 
  > proc.time()
     user  system elapsed 
     0.48    0.12    0.54 
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(cif)
  > 
  > test_check("cif")
  [ FAIL 0 | WARN 0 | SKIP 0 | PASS 0 ]
  > 
  > proc.time()
     user  system elapsed 
     0.87    0.10    0.96