[R] Memory usage decreases drastically after save workspace, quit, restart, load workspace
Dear all, I have the following problem: - I have written a program in R which runs out of physical memory on my MacBook Pro with 1.5 GB RAM - The memory usage is much higher then I would expect from the actual data in my global environment - When I save the workspace, quit R, restart R and load the workspace again, memory usage is much less then before (~50 MB instead of ~1.5 GB), although nothing seems to be missing. - It doesn't seem to be possible to reduce the amount of memory used by calling gc() - the behavior is independent of if I use R in command line or from GUI, so I can exclude a problem of the Mac GUI Any suggestions what the problem might be or how I could debug this? Thanks in advance for any help. Best regards, Klaus platform i386-apple-darwin8.6.1 arch i386 os darwin8.6.1 system i386, darwin8.6.1 status major 2 minor 3.1 year 2006 month 06 day01 svn rev38247 language R version.string Version 2.3.1 (2006-06-01) __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Problem with stepAIC, "invalid second argument"
Dear all, am I doing something wrong, or is this a bug (R 2.0.1 & 1.9.1, linux&windows): > library(MASS) > df <- data.frame(x = rnorm(100), y = rnorm(100), z = rnorm(100)) > f <- lm(x ~ y + z, data = df) > stepAIC(f, scope = list(upper = ~y * z, lower = ~1)) Start: AIC= -16.56 x ~ y + z Error in eval(expr, envir, enclos) : invalid second argument > I can avoid the problem doing the following, but in my application I do not want to use attach / detach because of possible name conflicts: > attach(df) > f <- lm(x ~ y + z) > stepAIC(f, scope = list(upper = ~y * z, lower = ~1)) Start: AIC= 23.55 x ~ y + z ... Call: lm(formula = x ~ 1) Coefficients: (Intercept) -0.1972 > detach(df) Thanks for any help, Klaus Thul __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Question to NLME, ML vs. REML
Dear all, I am planning to use nlme library for analysis of experiments in semiconductor industry. Currently I am using "lm" but plan to move to "lme" to handle within wafer / wafer-to-wafer and lot-to-lot variation correctly. So far everything is working well, but I have a fundamentel question: NLME offers "maximum likelihood" and "restricted maximum likelihood" for parameter estimation. ML has the advantage, that likelihood ratios can be computed even with changes in model structure. In addition, ML works with the stepAIC function from MASS-library which I am currently using for model-building. I am wondering, why REML is the default setting in NLME and therefore somehow preferred by the authors. What is the main reason to use REML? Maybe I am lacking here statistical knowledge. Any hint, including reference to literature would be very helpful. Best regards, Klaus Thul __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] residuals() with missing data, alignment to original data set
Hello, I have a data frame and want to do the following. Do a regression of one variable versus another: fit <- lm(a ~ b) Then I would like to plot the residuals of this fit versus a third variable: bwplot(residuals(fit) ~ group) This fails, if a or b contain NAs, because the data in the residuals() - vector are not aligned anymore to the data in the original data frame. Is there a way to solve this issue? Regards and thanks for your help Klaus __ [EMAIL PROTECTED] mailing list http://www.stat.math.ethz.ch/mailman/listinfo/r-help
