Re: [R] Formula aruguments with NLS and model.frame()

Fri, 29 Sep 2006 17:03:39 -0700 

Spencer,
Thank you for taking time to reply and offer suggestions. garchFit does not allow 'formula.mean=~z+arma(p, q)', nor does it allow xreg=(x,y) options. Any thing is xreg is ignored with a warming or error. I have debugged garchFit and I know where the code should be modified to implement other exogenous variable in the mean equation. Because mine is a NL mean equation I was looking at nls. You are correct, a simpler equation that would operate like the xreg for armaFit, would be the place to start. I also can use debug on my equation for the nls method to try and understand the model.frame and environment material. Thank you for the debug suggestion. 

Good luck
Joe


Spencer Graves wrote:

     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.






















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