StackOverflow2013

Note that there are some explanatory texts on larger screens.

plurals

POIteratively forecasting dyn models
primarykey
Id
4856555
data
AcceptedAnswerId
4858364
AnswerCount
1
ClosedDate
CommentCount
0
CommunityOwnedDate
CreationDate
2011-01-31T21:52:00.160
FavoriteCount
4
LastActivityDate
2011-02-01T02:42:08.957
LastEditDate
LastEditorUserId
0
OwnerUserId
345660
ParentId
0
PostTypeId
1
Score
3
ViewCount
1459
LastEditorDisplayName
text
Body
<p>I've written a function to iteratively forecast models built using the package dyn, and I'd like some feedback on it. Is there a better way to do this? Has someone written canonical "forecast" methods for the dyn class (or dynlm class), or am I venturing into uncharted territory here?</p> <pre><code>ipredict <-function(model, newdata, interval = "none", level = 0.95, na.action = na.pass, weights = 1) { P<-predict(model,newdata=newdata,interval=interval, level=level,na.action=na.action,weights=weights) for (i in seq(1,dim(newdata)[1])) { if (is.na(newdata[i])) { if (interval=="none") { P[i]<-predict(model,newdata=newdata,interval=interval, level=level,na.action=na.action,weights=weights)[i] newdata[i]<-P[i] } else{ P[i,]<-predict(model,newdata=newdata,interval=interval, level=level,na.action=na.action,weights=weights)[i,] newdata[i]<-P[i,1] } } } P_end<-end(P)[1]*frequency(P)+(end(P)[2]-1) #Convert (time,period) to decimal time P<-window(P,end=P_end-1*frequency(P)) #Drop last observation, which is NA return(P) } </code></pre> <p>Example usage:</p> <pre><code>library(dyn) y<-arima.sim(model=list(ar=c(.9)),n=10) #Create AR(1) dependant variable A<-rnorm(10) #Create independant variables B<-rnorm(10) C<-rnorm(10) Error<-rnorm(10) y<-y+.5*A+.2*B-.3*C+.1*Error #Add relationship to independant variables data=cbind(y,A,B,C) #Fit linear model model.dyn<-dyn$lm(y~A+B+C+lag(y,-1),data=data) summary(model.dyn) #Forecast linear model A<-c(A,rnorm(5)) B<-c(B,rnorm(5)) C<-c(C,rnorm(5)) y=window(y,end=end(y)+c(5,0),extend=TRUE) newdata<-cbind(y,A,B,C) P1<-ipredict(model.dyn,newdata) P2<-ipredict(model.dyn,newdata,interval="prediction") #Plot plot(y) lines(P1,col=2) </code></pre>
Tags
<r><dynamic><regression><forecasting>
Title
Iteratively forecasting dyn models
singulars
PostAcceptedAnswerId
1. PO
  singulars
  PostTypePostTypeId
  PTAnswer
PostParentId
1. This table or related slice is empty.
PostTypePostTypeId
1. PTQuestion
UserLastEditorUserId
1. This table or related slice is empty.
UserOwnerUserId
1. USZach
plurals
PostLinksPostIdRelatedPostId
1. This table or related slice is empty.
PostLinksRelatedPostIdPostId
1. PL
  singulars
  LinkTypeLinkTypeId
  LTLinked
PostsAcceptedAnswerId
1. This table or related slice is empty.
PostsParentIdCreationDate
1. PO
  singulars
  PostTypePostTypeId
  PTAnswer
VotesPostIdCreationDate
1. VO
  singulars
  PostPostId
  POIteratively forecasting dyn models
  UserUserId
  USZach
  VoteTypeVoteTypeId
  VTFavorite
2. VO
  singulars
  PostPostId
  POIteratively forecasting dyn models
  UserUserId
  This table or related slice is empty.
  VoteTypeVoteTypeId
  VTUpMod
3. VO
  singulars
  PostPostId
  POIteratively forecasting dyn models
  UserUserId
  USVishal Belsare
  VoteTypeVoteTypeId
  VTFavorite
CommentsPostId
1. This table or related slice is empty.

Querying!

Guidance

A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.