From: Rolf Turner <rolf_at_erdos.math.unb.ca>

Date: Fri 28 Jul 2006 - 22:10:21 EST

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. Received on Fri Jul 28 22:21:58 2006

Date: Fri 28 Jul 2006 - 22:10:21 EST

This is not a question of ``which R procedure'' but rather a question of understanding a bit about statistics and linear models. You say you are a ``master's student''; I hope you are not a master's student in *statistics*, given that you lack this (very) basic knowledge! If you are a student in some other discipline, I guess you may be forgiven. The ``R procedure'' that you need to use is just lm()! Briefly, what you need to do is combine your two data sets into a *single* data set (using rbind should work), add in a grouping variable (a factor with two levels, one for each measure procedure) e.g. my.data$gp <- factor(rep(c(1,2),c(n1,n2))) where n1 and n2 are the sample sizes for procedure 1 and procedure 2 respectively. Then fit linear models with formulae involving the grouping factor (``gp'') as well as the other predictors, and test for the ``significance'' of the terms in the model that contain ``gp''. You might start with fit <- lm(y~.*gp,data=my.data) anova(fit) where ``y'' is (of course) your reponse. You ought to study up on the underlying ideas of inference for linear models, and the nature of ``factors''. John Fox's book ``Applied Regression Analysis, Linear Models, and Related Methods'' might be a reasonable place to start. Bon chance. cheers, Rolf Turner rolf@math.unb.ca ______________________________________________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. Received on Fri Jul 28 22:21:58 2006

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