b. Since you wrote "y(t)" and you say you are new to this list, I feel compelled to confirm that you know that parentheses "(..)" signal a function call in S-Plus syntax. I would write your expression "y[t] = 0.03*x1[t]+1.5*x2[t]", etc. (And I would avoid "t", because that is is the R function for matrix transpose.)
c. I wouldn't try "stepAIC" with "lme" until I was reasonably confident of the "lme" noise model.
d. From a pragmatic perspective, I would not play with the correlation structure until I felt I had a reasonable model for the fixed effects. The reason is simple: Lack of fit can mascarade as a virtually nonstationary process, even if it is just something deterministic plus a small amouth of independent noise. For example, if x is slowly moving like, "x <- 1:99" or "x <- sin((1:99)/(20*pi))", then "y <- x + 0.01*rnorm(99)" fit without "x" will appear as nonstationary.
e. To understand autocorrelation structure (though not the "lme" function), I have gotten a lot from the book by Box, Jenkins and Reinsel (1994) Time Series Analysis: Forecasting & Control, 3rd Edition (Prentice Hall).
hope this helps. spencer graves
Hanhan wrote:
Hi,
I'm a new member here in the list. I am a graduate from
University of Georgia. Recently in doing analysis using lme on a dataset, I found several questions:
1. How to express the equation when the correlation
structure is very complicated. For exmaple, if the fixed is y(t)=0.03x1(t)+1.5x2(t)(I omitted "hat" and others). And the model with corARMA(p=2,q=3) is proper. What will be the complete equation?
2. Is is that any regression error will be stationary?
(Forgive me for my poor math background. This may be a simple question to most people.) Since corARIMA is not available.
3. Why not make a function to automatically select the best
corARMA structure (setting max p and q and the computer takes care of the rest)?
4. When the initial model (without considering correlation
structure) has many variables and some have no significance, should I use stepAIC first to eliminate some variables? Or, try corARMA with different combination of p and q, which may make more variables significant without having to reduce variable. I prefer the latter.
5. If the best corARMA model out of a model still contain
some insignificant variables, I would use drop1 (instead of stepAIC) and then try all the possible corARMA structures again. So my steps would be drop1, corARMA, drop1,corARMA, til I get a model with all variables significant. If the initial model has many variables, it would be a time-consuming process. Is it proper to do so? If proper, it'll be wonderful if a new function is developed to automatically do so.
Thanks, Hanhan
Xianglu Han
206 Environmental Health Science
University of Georgia 30602
Phone: 706 255 2308
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