From joe-byers at utulsa.edu  Thu Sep 21 19:50:19 2006
From: joe-byers at utulsa.edu (Joe Byers)
Date: Thu, 21 Sep 2006 12:50:19 -0500
Subject: [Rd] Help understanding how nls parses the formula argument to
 estimate the model
Message-ID: <eeujbo$ooo$1@sea.gmane.org>

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 model.frame operations with the eval functions.

thank you




[Rd] Help understanding how nls parses the formula argument to estimate the model
Joe Byers