Google offers [1], which probably seems like a vague response but your question omitted a reproducible example and is contaminated by posting in HTML (read the Posting Guide).
[1] https://www.rdocumentation.org/packages/MLmetrics/versions/1.1.1/topics/MAPE On July 2, 2018 1:22:39 PM PDT, Paul Bernal <paulberna...@gmail.com> wrote: >Dear friends, > >I want to extract the MAPE value from a fitted time series model. This >is >what I have: > >> str(TransitSpline) >List of 12 > $ method : chr "Cubic Smoothing Spline" > $ level : num [1:2] 80 95 >$ x : Time-Series [1:385] from 1 to 385: 77 75 85 74 >73 >96 82 90 91 81 ... > $ series : chr "data$Transits" >$ mean : Time-Series [1:10, 1] from 386 to 395: 186 178 >170 163 155 ... > ..- attr(*, "dimnames")=List of 2 > .. ..$ : NULL > .. ..$ : chr "Series 1" >$ upper : Time-Series [1:10, 1:2] from 386 to 395: 202 >199 >197 197 197 ... > ..- attr(*, "dimnames")=List of 2 > .. ..$ : NULL > .. ..$ : chr [1:2] "Series 1" "Series 2" >$ lower : Time-Series [1:10, 1:2] from 386 to 395: 171 >158 >144 129 113 ... > ..- attr(*, "dimnames")=List of 2 > .. ..$ : NULL > .. ..$ : chr [1:2] "Series 1" "Series 2" > $ model :List of 2 > ..$ beta: num 6.15 > ..$ call: language splinef(y = data$Transits) >$ fitted : Time-Series [1:385] from 1 to 385: 76.1 77.3 >78.5 >80.1 82.2 ... >$ residuals : Time-Series [1:385] from 1 to 385: NA -1.3 >9.49 >-8.64 -4.34 ... > $ standardizedresiduals: Time-Series [1:385] from 1 to 385: NA -0.875 >6.517 -5.586 -2.736 ... >$ onestepf : Time-Series [1:385] from 1 to 385: NA 76.3 >75.5 >82.6 77.3 ... > - attr(*, "class")= chr [1:2] "splineforecast" "forecast" > > >> str(summary(TransitSpline)) >#Here I want to get the value for the MAPE measure >Forecast method: Cubic Smoothing Spline > >Model Information: >$`beta` >[1] 6.149167 > >$call >splinef(y = data$Transits) > > >Error measures: > ME RMSE MAE MPE MAPE MASE > ACF1 >Training set -0.07776434 12.10204 9.003675 -0.2408687 5.377131 0.930913 >-0.2766975 > >Forecasts: > Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 >386 186.0153 170.52426 201.5064 162.323777 209.7069 >387 178.2220 157.87687 198.5671 147.106804 209.3372 >388 170.4287 143.80863 197.0487 129.716832 211.1405 >389 162.6353 128.61257 196.6581 110.602006 214.6687 >390 154.8420 112.52646 197.1576 90.125956 219.5581 >391 147.0487 95.66491 198.4324 68.463984 225.6334 >392 139.2553 78.10706 200.4036 45.737114 232.7736 >393 131.4620 59.92462 202.9994 22.055013 240.8690 >394 123.6687 41.14798 206.1894 -2.535833 249.8732 >395 115.8753 21.82457 209.9261 -27.962900 259.7136 >'data.frame': 10 obs. of 5 variables: > $ Point Forecast: num 186 178 170 163 155 ... > $ Lo 80 : num 171 158 144 129 113 ... > $ Hi 80 : num 202 199 197 197 197 ... > $ Lo 95 : num 162.3 147.1 129.7 110.6 90.1 ... > $ Hi 95 : num 210 209 211 215 220 ... > >any idea on how to accomplish this? > >Best regards, > >Paul > > [[alternative HTML version deleted]] > >______________________________________________ >R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code. -- Sent from my phone. Please excuse my brevity. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
