Dear Burak, Since two of the explanatory variables are quantitative, it is unusual to call this a three-way ANOVA (as opposed to a dummy-variable regression or analysis of covariance). I'd also think about fitting the model with lm() rather than aov(), so that you can more easily see the regression coefficients, and about whether you really want F-tests based on sequential sums of squares.
In any event, you can get adjusted means and their standard errors from the effects package. I hope this helps, John -------------------------------- John Fox, Professor Department of Sociology McMaster University Hamilton, Ontario, Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > project.org] On Behalf Of burak pekin > Sent: March-26-08 4:50 AM > To: r-help@r-project.org > Subject: [R] adjusted means and adjusted standard errors after ANOVA > > I am trying to obtain adjusted means and standard errors for a three > way > ANOVA > > > > I have three effects, two continuous; fire frequency and annual > precipitation, and one categorical; soil type in an unbalanced design. > > > > I am testing the effect of annual precipition (AP), soil type (ST), and > fire > frequency (FF) on stem count (SCt) > > > > My data table looks as such: > > > > > > > ST > > FF > > AP > > SCt > > > 3 > > Coy > > 4 > > 888 > > 312 > > > 4 > > Coy > > 3 > > 911 > > 185 > > > 6 > > Coy > > 3 > > 937 > > 136 > > > 7 > > Coy > > 5 > > 1011 > > 42 > > > 8 > > Coy > > 4 > > 1015 > > 138 > > > 9 > > Cop > > 4 > > 950 > > 290 > > > 11 > > Cop > > 4 > > 951 > > 252 > > > 16 > > Coy > > 4 > > 988 > > 124 > > > 17 > > Coy > > 5 > > 988 > > 118 > > > 20 > > Coy > > 5 > > 1000 > > 242 > > > 24 > > Cop > > 3 > > 901 > > 220 > > > 25 > > Cop > > 2 > > 929 > > 238 > > > 26 > > Cop > > 2 > > 954 > > 133 > > > 27 > > Cop > > 1 > > 934 > > 180 > > > 28 > > Cop > > 1 > > 938 > > 119 > > > 30 > > Cop > > 2 > > 918 > > 195 > > > > My R output for a 3 way ANOVA is as such: > > > > > SCt.aov = aov (SCt ~ AP + ST + FF, data) > > > summary ( SCt.aov ) > > > > Df Sum Sq Mean Sq F value Pr(>F) > > AP 1 23696 23696 8.4237 0.01327 * > > ST 1 313 313 0.1114 0.74429 > > FF 1 21532 21532 7.6544 0.01707 * > > Residuals 12 33757 2813 > > --- > > Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > > > > > > > I would like to present my data so that it shows the significance of > the p > value for FF after the variability of AP and ST have been taken out, so > I > will need R to output the adjusted means and standard errors. This I do > not > know how to do. What is the easiest way to do this in R from this > analysis? > > > > Kind regards, > > Burak Pekin > > > > > > Burak Pekin > > Ecosystem Research Group > > School of Plant Biology (M090) > > University of Western Australia > > 35 Stirling Highway > Crawley, WA 6009 Australia > Ph: +61 08 6488 7923 > Fax: +61 08 6488 1001 > > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org 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-help@r-project.org 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.
