Build quantmod model given specified fitting method
Construct and attach a fitted model of type method to quantmod object.
buildModel(x, method, training.per, ...)
x |
An object of class |
training.per |
character vector representing dates in ISO 8601 format “CCYY-MM-DD” or “CCYY-MM-DD HH:MM:SS” of length 2 |
method |
A character string naming the fitting method. See details section for available methods, and how to create new methods. |
... |
Additional arguments to method call |
Currently available methods include:
lm, glm, loess, step, ppr, rpart[rpart], tree[tree], randomForest[randomForest], mars[mda], polymars[polspline], lars[lars], rq[quantreg], lqs[MASS], rlm[MASS], svm[e1071], and nnet[nnet].
The training.per should match the undelying date
format of the time-series data used in modelling. Any other style
may not return what you expect.
Additional methods wrappers can be created to allow for modelling
using custom functions. The only requirements are for a wrapper
function to be constructed taking parameters quantmod,
training.data, and .... The function must return the
fitted model object and have a predict method available.
It is possible to add predict methods if non exist by
adding an S3 method for predictModel. The
buildModel.skeleton function can be used for new methods.
An object of class quantmod with fitted model attached
See buildModel.skeleton for information on adding additional methods
Jeffrey Ryan
## Not run:
getSymbols('QQQQ',src='yahoo')
q.model = specifyModel(Next(OpCl(QQQQ)) ~ Lag(OpHi(QQQQ),0:3))
buildModel(q.model,method='lm',training.per=c('2006-08-01','2006-09-30'))
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.