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Hello R gurus, I have a simple routine using for loops that i was just trying to make it faster y <- rnorm(10) Nindiv <- 10 x <-matrix(sample(c(0:2),50,re = T),10) gtemp <- rnorm(10) ycorr <- array(0,c(Nindiv,1)) for (i in 1:Nindiv) { for (k in 1:ncol(x)) { ycorr[i] <- y[i] - x[i,k]*gtemp[k] } } It works fine but I wanted to make it faster and I think an apply function could probably solve this problem. Any suggestions? Thanks -- View this message in...

..."? At first, I thought that this was describing possible correlations among the predictor variables, but then I saw that it also included the model intercept. What do these correlation value mean? ##More detailed information ##function calls: sppl.i.xx = gls(all.all.rch~l10area+newx, data = gtemp, method="ML") sppl.i.ex = gls(all.all.rch~l10area+newx, data = gtemp, method="ML", correlation = corExp(c(20,.8), form=~x+y|area, nugget=TRUE)) ##model summaries > summary(sppl.i.xx) Generalized least squares fit by maximum likelihood Model: all.all.rch ~...

...of 559.2, it seems that gls believes it has estimated 6 parameters in the fitting process. Again, is this an error in gls, or an error on my part? Copied from R terminal: > > summary(sppl.i.ex) Generalized least squares fit by maximum likelihood Model: all.all.rch ~ l10area + newx Data: gtemp AIC BIC logLik 559.167 575.7911 -273.5835 Correlation Structure: Exponential spatial correlation Formula: ~x + y | area Parameter estimate(s): range nugget 15.4448835 0.3741476 Coefficients: Value Std.Error t-value p-value (Intercept) 7.621306 0.764813...