Printing function for MARSS Predict objects
print_marssPredict.RdMARSS() outputs marssMLE objects. predict(object), where object is marssMLE object, will return the predictions of \(\mathbf{y}_t\) or the smoothed value of \(\mathbf{x}_t\) for h steps past the end of the model data. predict(object) returns a marssPredict object which can be passed to print.marssPredict() for automatic printing.
Usage
# S3 method for marssPredict
print(x, ...)Arguments
- x
A
marssPredictobject.- ...
Other arguments for print. Not used.
Examples
dat <- t(harborSealWA)
dat <- dat[2:4,] #remove the year row
fit <- MARSS(dat, model=list(R="diagonal and equal"))
#> Success! abstol and log-log tests passed at 44 iterations.
#> Alert: conv.test.slope.tol is 0.5.
#> Test with smaller values (<0.1) to ensure convergence.
#>
#> MARSS fit is
#> Estimation method: kem
#> Convergence test: conv.test.slope.tol = 0.5, abstol = 0.001
#> Estimation converged in 44 iterations.
#> Log-likelihood: 17.84491
#> AIC: -15.68982 AICc: -10.45173
#>
#> Estimate
#> R.diag 0.00582
#> U.X.SJF 0.06833
#> U.X.SJI 0.07084
#> U.X.EBays 0.04221
#> Q.(X.SJF,X.SJF) 0.04150
#> Q.(X.SJI,X.SJI) 0.01271
#> Q.(X.EBays,X.EBays) 0.00807
#> x0.X.SJF 5.97602
#> x0.X.SJI 6.70656
#> x0.X.EBays 6.63306
#> Initial states (x0) defined at t=0
#>
#> Standard errors have not been calculated.
#> Use MARSSparamCIs to compute CIs and bias estimates.
#>
# 2 steps ahead forecast
predict(fit, type="ytT", n.ahead=2)
#> .rownames t estimate
#> 23 SJF 23 7.547435
#> 24 SJF 24 7.615761
#> 47 SJI 23 8.335404
#> 48 SJI 24 8.406240
#> 71 EBays 23 7.603746
#> 72 EBays 24 7.645959
# smoothed x estimates with intervals
predict(fit, type="xtT")
#> .rownames t .x estimate
#> 1 X.SJF 1 6.044264 6.044345
#> 2 X.SJF 2 6.192149 6.112590
#> 3 X.SJF 3 6.340035 6.260476
#> 4 X.SJF 4 6.487920 6.408361
#> 5 X.SJF 5 6.635806 6.556246
#> 6 X.SJF 6 6.783691 6.704132
#> 7 X.SJF 7 6.934182 6.852017
#> 8 X.SJF 8 7.097030 7.002508
#> 9 X.SJF 9 6.805200 7.165357
#> 10 X.SJF 10 6.948989 6.873526
#> 11 X.SJF 11 7.273468 7.017315
#> 12 X.SJF 12 7.229726 7.341794
#> 13 X.SJF 13 7.072488 7.298052
#> 14 X.SJF 14 7.142899 7.140814
#> 15 X.SJF 15 7.367541 7.211225
#> 16 X.SJF 16 7.609002 7.435867
#> 17 X.SJF 17 7.379654 7.677329
#> 18 X.SJF 18 7.680866 7.447980
#> 19 X.SJF 19 7.615123 7.749192
#> 20 X.SJF 20 7.693577 7.683449
#> 21 X.SJF 21 7.486276 7.761903
#> 22 X.SJF 22 7.479109 7.554602
#> 23 X.SJI 1 6.777215 6.777395
#> 24 X.SJI 2 6.912514 6.848051
#> 25 X.SJI 3 7.047813 6.983350
#> 26 X.SJI 4 7.183112 7.118648
#> 27 X.SJI 5 7.318411 7.253947
#> 28 X.SJI 6 7.453709 7.389246
#> 29 X.SJI 7 7.637902 7.524545
#> 30 X.SJI 8 7.590312 7.708738
#> 31 X.SJI 9 7.679125 7.661148
#> 32 X.SJI 10 7.737608 7.749960
#> 33 X.SJI 11 7.907332 7.808443
#> 34 X.SJI 12 7.975191 7.978168
#> 35 X.SJI 13 8.061066 8.046027
#> 36 X.SJI 14 8.154990 8.131902
#> 37 X.SJI 15 8.230627 8.225826
#> 38 X.SJI 16 8.373657 8.301463
#> 39 X.SJI 17 8.421790 8.444493
#> 40 X.SJI 18 8.477634 8.492626
#> 41 X.SJI 19 8.512724 8.548470
#> 42 X.SJI 20 8.398618 8.583560
#> 43 X.SJI 21 8.366571 8.469454
#> 44 X.SJI 22 8.264568 8.437407
#> 45 X.EBays 1 6.674960 6.675270
#> 46 X.EBays 2 6.783692 6.717173
#> 47 X.EBays 3 6.892424 6.825905
#> 48 X.EBays 4 7.001156 6.934636
#> 49 X.EBays 5 7.109887 7.043368
#> 50 X.EBays 6 7.218619 7.152100
#> 51 X.EBays 7 7.345337 7.260831
#> 52 X.EBays 8 7.321332 7.387549
#> 53 X.EBays 9 7.388396 7.363545
#> 54 X.EBays 10 7.458986 7.430609
#> 55 X.EBays 11 7.517168 7.501198
#> 56 X.EBays 12 7.528953 7.559380
#> 57 X.EBays 13 7.557326 7.571165
#> 58 X.EBays 14 7.585700 7.599539
#> 59 X.EBays 15 7.635923 7.627913
#> 60 X.EBays 16 7.665752 7.678135
#> 61 X.EBays 17 7.669217 7.707964
#> 62 X.EBays 18 7.671004 7.711430
#> 63 X.EBays 19 7.723585 7.713217
#> 64 X.EBays 20 7.625414 7.765797
#> 65 X.EBays 21 7.555667 7.667626
#> 66 X.EBays 22 7.561534 7.597879