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...the confidence interval for the individual predicted Y. According to the stats text I looked at, I need to change the above formula to se.fit<-sqrt(apply(gradient, 1, function(x) sum(vcov(myNLSmodel)*outer(x,x))+NEWTERM)) where NEWTERM is NEWTERM<-myNLSmodel$m$deviance()/(nObservations - nParameters) So the problem is that while the confidence intervals are close to those provided by SAS, they are always smaller. This, in the end, makes a large difference for the application. Can anyone suggest how I might make the confidence intervals I calculate in R match up with those of SAS...

Hello, everybody: I recently encountered an example with in which the graph was placed in a way that did not leave room for a legend. Maybe you would describe it as "legend too big", I'm not sure. I found myself wishing I could force in some space after the title. Here's working example where I'd like to make room for a legend. x <- rnorm(100) hist(x, freq=F,