I used SAS to analyze the data initially, since the data set was made up of several files when I received it, and I'm still not very good at manipulating data in R.
I have posted the data set from one location at the following address: http://uwstudentweb.uwyo.edu/A/AKNISS/sxherb.txt var=cultivar trt=herbicide treatment yield=response variable of interest All plot# from 101 to 104 are rep 1, 201-204 rep 2, and 301 to 304 rep 3. It was the only file that was in an easy format for R to read at the moment, and was probably the most reliable trial of the two locations. I would like to use power.anova.test() with this data set to plan next years study (to get a sample size for each herb*var combination), but I'm not quite sure how that is done for an interaction effect. Do I just use the MS for herb*var as the between group variance and the MSE as the within group variance? Or do I need to somehow include other variance parameters in the model? The model for this location (split-block design): yield = rep + herb + var + herb*var ## all are fixed effects rep*herb = error term for herb rep*var = error term for cultivar residual = error term for herb*var I hope this attempt at my question was a little more clear. I appreciate any help that is offered. Andrew Kniss Assistant Research Scientist University of Wyoming Department of Plant Sciences 1000 E. Univesity Ave Laramie, WY 82071 USA [EMAIL PROTECTED] -----Original Message----- From: John Maindonald [mailto:[EMAIL PROTECTED] Sent: Tuesday, February 22, 2005 3:37 PM To: [email protected] Cc: [EMAIL PROTECTED] Subject: Re: R-help Digest, Vol 24, Issue 22 You need to give the model formula that gave your output. There are two sources of variation (at least), within and between locations; though it looks as though your analysis may have tried to account for this (but if so, the terms are not laid out in a way that makes for ready interpretation. The design is such (two locations) that you do not have much of a check that effects are consistent over locations. You need to check whether results really are similar for all cultivars and for all herbicides, so that it is legitimate to pool as happens in the overall analysis. If a herbicide:cultivar combination has little effect the variability may be large, while if it has a dramatic effect (kills everything!), there may be no variability to speak of. John Maindonald. On 22 Feb 2005, at 10:06 PM, [EMAIL PROTECTED] wrote: > To: "'Bob Wheeler'" <[EMAIL PROTECTED]> > Cc: [email protected] > Subject: RE: [R] power.anova.test for interaction effects > Reply-To: [EMAIL PROTECTED] > > > It's a rather complex model. A 37*4 factorial (37 cultivars[var]; 4 > herbicide treatments[trt]) with three replications[rep] was carried > out at > two locations[loc], with different randomizations within each rep at > each > location. > > Source DF Error Term MS > Loc 1 Trt*rep(loc) 12314 > Rep(loc) 4 Trt*rep(loc) 1230.5 > Trt 3 Trt*rep(loc) 64.72 > Trt*loc 3 Trt*rep(loc) 33.42 > Trt*rep(loc) 12 Residual 76.78 > Var 36 Var*trt*loc 93.91 > Var*trt 108 Var*trt*loc 12.06 > Var*trt*loc 144 Residual 43.09 > Residual 575 NA 21.23 > > > -----Original Message----- > From: Bob Wheeler [mailto:[EMAIL PROTECTED] > Sent: Monday, February 21, 2005 4:33 PM > To: [EMAIL PROTECTED] > Cc: [email protected] > Subject: Re: [R] power.anova.test for interaction effects > > Your F value is so low as to make me suspect your model. Where did the > 144 denominator degrees of freedom come from? > John Maindonald email: [EMAIL PROTECTED] phone : +61 2 (6125)3473 fax : +61 2(6125)5549 Centre for Bioinformation Science, Room 1194, John Dedman Mathematical Sciences Building (Building 27) Australian National University, Canberra ACT 0200. ______________________________________________ [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
