rpy2 des not want to work with tseries. I am using the latest rpy2 version.
What do I do to estimate a time series model? Why do I get invalid lag?
import rpy2.robjects as robjects
from rpy2.robjects.packages import importr
stats = importr('stats')
base = importr('base')
tseries = importr('tseries')
imps = robjects.FloatVector([1,3.1,5,6,4,8,1])
robjects.globalenv["imps"] = imps
aa = tseries.arma('imps,order=c(1,0)')
Error in function (x, order = c(1, 1), lag = NULL, coef = NULL,
include.intercept = TRUE, :
invalid lag
Traceback (most recent call last):
File "/Users/dalandmontgomery/Documents/Aptana Studio 3
Workspace/aws_hive/time_series/R-arima_test.py", line 89, in <module>
aa = tseries.arma('imps,order=c(1,0)')
File
"/Library/Python/2.6/site-packages/rpy2-2.2.0beta3dev_20110515-py2.6-macosx-10.6-universal.egg/rpy2/robjects/functions.py",
line 82, in __call__
return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
File
"/Library/Python/2.6/site-packages/rpy2-2.2.0beta3dev_20110515-py2.6-macosx-10.6-universal.egg/rpy2/robjects/functions.py",
line 34, in __call__
res = super(Function, self).__call__(*new_args, **new_kwargs)
rpy2.rinterface.RRuntimeError: Error in function (x, order = c(1, 1),
lag = NULL, coef = NULL, include.intercept = TRUE, :
invalid lag
------------------------------------------------------------------------------
Achieve unprecedented app performance and reliability
What every C/C++ and Fortran developer should know.
Learn how Intel has extended the reach of its next-generation tools
to help boost performance applications - inlcuding clusters.
http://p.sf.net/sfu/intel-dev2devmay
_______________________________________________
rpy-list mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/rpy-list
