R help - Sep 2006 - Help understanding how nls parses the formula argument to estimate the model

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.

thank you