I haven't seen any replies to this post, so I will offer a couple
of comments.
First, your example is entirely too complicated and not self
contained for me to say much in the limited time I have for this. I
suggest you start by splitting your problem into several steps. For
example, will 'garchFit' allow 'formula.mean' to be something
other than
'~arma(p, q)', where p and q are nonnegative integers? If no, I suggest
you start by trying to produce your own modification to 'garchFit' so it
will accept something like 'formula.mean=~z+arma(p, q)'; I suggest you
give your local copy a different name like 'garchFitZ'. Second, I
suggest you cut your example data down to a minimum, e.g, 9 or 20
observations, just enough so the algorithm won't die for some reason
that would not occur with a larger data set but small enough so you can
quickly print out and study every object the 'garchFit' algorithm
produces.
Second, I suggest you use 'debug' to walk through your local
version of 'garchFit' line by line. I've found this to be a very
powerful way to learn what happens in the internal environment of a
function.
If you get stuck trying this, please submit another post including
commented, minimal, self-contained, reproducible code, as suggested in
the posting guide 'www.R-project.org/posting-guide.html'.
Hope this helps.
Spencer Graves
and provide commented, minimal, self-contained, reproducible code.
Joe W. Byers wrote:> I could use some help understanding how nls parses the formula argument
> to a model.frame and estimates the model. I am trying to utilize the
> functionality of the nls formula argument to modify garchFit() to handle
> other variables in the mean equation besides just an arma(u,v)
> specification.
>
> My nonlinear model is
> y<-nls(t~a*sin(w*2*pi/365*id+p)+b*id+int,data=t1,
> start=list(w=.5,a=.1,p=.5,b=init.y$coef[2],int=init.y$coef[1] ),
> control=list(maxiter=1000000,minFactor=1e-18))
> where t is change in daily temperatures, id is just a time trend and the
> a*sin is a one year fourier series.
>
> I have tried to debug the nls code using the following code
> t1<-data.frame(t=as.vector(x),id=index(x))
> data=t1;
> formula <- as.formula(t ~ a *sin(w *2* pi/365 * id + p) + b * id + int);
> varNames <- all.vars(formula)
> algorithm<-'default';
> mf <- match.call(definition=nls,expand.dots=FALSE,
> call('nls',formula, data=parent.frame(),start,control =
nls.control(),
> algorithm = "default", trace = FALSE,
> subset, weights, na.action, model = FALSE, lower = -Inf,
> upper = Inf));
> mWeights<-F;#missing(weights);
> start=list(w=.5,a=.1,p=.5,b=init.y$coef[2],int=init.y$coef[1] );
> pnames <- names(start);
> varNames <- varNames[is.na(match(varNames, pnames, nomatch =
NA))]
>
> varIndex <- sapply(varNames,
> function(varName, data, respLength) {
> length(eval(as.name(varName), data))%%respLength == 0},
> data, length(eval(formula[[2]], data))
> );
> mf$formula <- as.formula(paste("~", paste(varNames[varIndex],
> collapse = "+")), env = environment(formula));
> mf$start <- NULL;mf$control <- NULL;mf$algorithm <- NULL;
> mf$trace <- NULL;mf$model <- NULL;
> mf$lower <- NULL;mf$upper <- NULL;
> mf[[1]] <- as.name("model.frame");
> mf<-evalq(mf,data);
> n<-nrow(mf)
> mf<-as.list(mf);
> wts <- if (!mWeights)
> model.weights(mf)
> else rep(1, n)
> if (any(wts < 0 | is.na(wts)))
> stop("missing or negative weights not allowed")
>
> m <- switch(algorithm,
> plinear = nlsModel.plinear(formula, mf, start, wts),
> port = nlsModel(formula, mf, start, wts, upper),
> nlsModel(formula, mf, start, wts));
>
> I am struggling with the environment issues associated with performing
> these operations. I did not include the data because it is 9000
> observations of temperature data. If anyone would like the data, I can
> provide it or a subset in a csv file.
>
>
> thank you
> Joe
>
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> and provide commented, minimal, self-contained, reproducible code.
>