### Eclipse Workspace Patch 1.0
#P lme4
Index: R/lmer.R
===================================================================
--- R/lmer.R	(revision 1697)
+++ R/lmer.R	(working copy)
@@ -78,7 +78,7 @@
 lmer <- function(formula, data, REML = TRUE, sparseX = FALSE,
                  control = list(), start = NULL,
                  verbose = 0L, subset, weights, na.action, offset,
-                 contrasts = NULL, devFunOnly=FALSE,
+                 contrasts = NULL, devFunOnly=FALSE, doFit = TRUE,
                  optimizer="Nelder_Mead", ...)
 {
     if (sparseX) warning("sparseX = TRUE has no effect at present")
@@ -133,11 +133,12 @@
     devfun <- mkdevfun(rho, 0L)
     devfun(reTrms$theta) # one evaluation to ensure all values are set
 
+	if (!doFit) return(list(rho=rho, devfun=devfun, reTrms=reTrms, optimizer=optimizer, control=control, fr=fr, mc=mc, verbose=verbose))
     if (devFunOnly) return(devfun)
 
     opt <- optwrap(optimizer,
                    devfun, rho$pp$theta, lower=reTrms$lower, control=control,
-                   rho=rho, adj=FALSE)
+                   rho=rho, adj=FALSE, verbose=verbose)
 
     mkMerMod(environment(devfun), opt, reTrms, fr, mc)
 }## { lmer }
@@ -263,7 +264,7 @@
 glmer <- function(formula, data, family = gaussian, sparseX = FALSE,
                   control = list(), start = NULL, verbose = 0L, nAGQ = 1L,
                   compDev = TRUE, subset, weights, na.action, offset,
-                  contrasts = NULL, mustart, etastart, devFunOnly = FALSE,
+                  contrasts = NULL, mustart, etastart, devFunOnly = FALSE, doFit = TRUE,
                   tolPwrss = 1e-10, optimizer=c("bobyqa","Nelder_Mead"), ...)
 {
     verbose <- as.integer(verbose)
@@ -348,7 +349,8 @@
     if (length(optimizer)==1) {
        optimizer <- replicate(2,optimizer)
      }
-    opt <- optwrap(optimizer[[1]],devfun,rho$pp$theta, rho$lower,
+	 if (!doFit) return(list(rho=rho, devfun=devfun, reTrms=reTrms, optimizer=optimizer, control=control, fr=fr, mc=mc, nAGQ=nAGQ, family=family, verbose=verbose))
+	 opt <- optwrap(optimizer[[1]],devfun,rho$pp$theta, rho$lower,
                    control=control, rho=rho,
                    adj=FALSE, verbose=verbose)
 
@@ -404,7 +406,7 @@
 ##' @export
 nlmer <- function(formula, data, control = list(), start = NULL, verbose = 0L,
                   nAGQ = 1L, subset, weights, na.action, offset,
-                  contrasts = NULL, devFunOnly = 0L, tolPwrss = 1e-10,
+                  contrasts = NULL, devFunOnly = 0L, tolPwrss = 1e-10, doFit = TRUE,
                   optimizer="Nelder_Mead", ...)
 {
     vals <- nlformula(mc <- match.call())
@@ -432,6 +434,7 @@
     if (length(optimizer)==1) {
       optimizer <- replicate(2,optimizer)
     }
+	if (!doFit) return(list(rho=rho, devfun=devfun, reTrms=reTrms, optimizer=optimizer, control=control, fr=fr, mc=mc, nAGQ=nAGQ, verbose=verbose))
 
     opt <- optwrap(optimizer[[1]],devfun, rho$pp$theta, rho$lower,
                    control=control, rho=rho, adj=FALSE)
@@ -458,6 +461,43 @@
     mkMerMod(environment(devfun), opt, vals$reTrms, vals$frame, mc)
 }## {nlmer}
 
+lmer_finalize = function(rho, optimizer, devfun, reTrms, control, fr, mc, verbose)
+{
+	opt <- optwrap(optimizer,
+			devfun, rho$pp$theta, lower=reTrms$lower, control=control,
+			rho=rho, adj=FALSE, verbose=verbose)
+	
+	mkMerMod(environment(devfun), opt, reTrms, fr, mc)
+}## { lmer_finalize }
+
+glmer_finalize = function(rho, optimizer, devfun, reTrms, control, fr, mc, nAGQ, verbose)
+{
+	opt <- optwrap(optimizer[[1]],devfun,rho$pp$theta, rho$lower,
+			control=control, rho=rho,
+			adj=FALSE, verbose=verbose)
+	
+	rho$nAGQ <- nAGQ
+	if (nAGQ > 0L) {
+		rho$lower <- c(rho$lower, rep.int(-Inf, length(rho$beta0)))
+		rho$u0    <- rho$pp$u0
+		rho$beta0 <- rho$pp$beta0
+		rho$dpars <- seq_along(rho$pp$theta)
+		if (nAGQ > 1L) {
+			if (length(reTrms$flist) != 1L || length(reTrms$cnms[[1]]) != 1L)
+				stop("nAGQ > 1 is only available for models with a single, scalar random-effects term")
+			rho$fac <- reTrms$flist[[1]]
+		}
+		devfun <- mkdevfun(rho, nAGQ)
+		#if (devFunOnly) return(devfun)
+		
+		opt <- optwrap(optimizer[[2]],devfun,c(rho$pp$theta, rho$beta0),
+				rho$lower, control=control, rho=rho,
+				adj=TRUE, verbose=verbose)
+	}
+	
+	mkMerMod(environment(devfun), opt, reTrms, fr, mc)
+}## { glmer_finalize }
+
 ##' Create a deviance evaluation function from a predictor and a response module
 ##'
 ##' From an merMod object create an R function that takes a single argument,