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 rpy-list@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/rpy-list