Thanks to all for your help, especially to Adaikalavan for his excellent and concise response. The key (for me) was realizing that the one variable that I am measuring is the dependent variable, and the groups are the independent variable. The model can then be easily represented in R with fit <- lm( variable ~ grp ).
Thanks again! - Jason On Mar 10, 2006, at 11:57 AM, Adaikalavan Ramasamy wrote: > Suppose you have 6 groups (A, B, C, D, E, F) and you measured the > weight > of 5 individuals from each group. Therefore you have 30 weight > observations in total. > > You wish to test if the mean of the response variable is different for > each of the groups. > [ i.e. the null hypothesis is that all 6 groups means are the same. ] > > > Lets simulate some data first: > > grp <- gl(6, k=5, labels=LETTERS[1:6]) > grp > [1] A A A A A B B B B B C C C C C D D D D D E E E E E F F F F F > Levels: A B C D E F > > set.seed(1) # for reproducibility only > w <- runif(30, min=40, max=75) # weights > w <- round(w, digits=1) > > > Let us first calculate the group means: > > tapply(w, grp, mean) > A B C D E F > 56.24 62.36 55.54 63.54 55.34 53.94 > > The group means are close, except for possibly group B and D. > > > You can do a formal testing by regressing the response (weight) to its > predictors (group). You will need to use the lm() function in R. > > fit <- lm( w ~ grp ) > > > You can get a summary of the fit by > > summary(fit) > ... > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 56.240 4.725 11.903 1.48e-11 *** > grpB 6.120 6.682 0.916 0.369 > grpC -0.700 6.682 -0.105 0.917 > grpD 7.300 6.682 1.093 0.285 > grpE -0.900 6.682 -0.135 0.894 > grpF -2.300 6.682 -0.344 0.734 > ... > > This simply says that the intercept is strongly NOT zero. Based on the > p-values, one can roughly summarise that none of the groups appear > to be > different. > > > Another useful tool is the ANOVA test which tests if the between group > variations are larger than average within group variation. > > anova(fit) > Analysis of Variance Table > > Response: w > Df Sum Sq Mean Sq F value Pr(>F) > grp 5 411.15 82.23 0.7367 0.6033 > Residuals 24 2678.79 111.62 > > This says that there is no significant variation between the groups. > > Hope this helps. > > Regards, Adai > > > > On Fri, 2006-03-10 at 11:24 -0500, Jason Horn wrote: >> I'd like to do a simple one-way ANOVA comparing the means of 6 >> groups. But it seems like the only way to do an ANOVA in R is to >> specify some sort of model, where there is an outcome or dependent >> variable that is a function of independent variables (linear model). >> But I don't have a linear model, I just want to do a simple ANOVA >> (and f-test) to compare the means. How do I do this? My stats >> skills are basic, so please bear with me. >> >> Thanks for any ideas... >> >> ______________________________________________ >> 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-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