summary(lm1) will show you R2 and adjusted R2 too. Ronggui
2009/7/26 Steve Lianoglou <mailinglist.honey...@gmail.com>: > Hi Sarah, > > On Jul 25, 2009, at 8:25 AM, Buckmaster, Sarah wrote: > >> Hi everyone, >> >> I have a question about calculating r-squared in R. I have tried searching >> the archives and couldn't find what I was looking for - but apologies if >> there is somewhere I can find this... >> >> I carried out a droughting experiment to test plant competition under >> limited water. I had: >> - 7 different levels of watering treatment (1 -7 - from most watered to >> least watered/) >> - 15 replicates at each level. >> >> Soil moisture readings were taken 4 times throughout the experiment (so I >> have 105 readings for each of the 4 times) and I now want to check that >> there was a significant decrease in soil moisture as I decreased the >> watering frequency, i.e. watering level 7 showed lower soil moisture units >> than level 1. >> >> I have carried out a repeated measures anova as follows (where block is >> which time the reading was taken: 1,2,3 or 4): >> model1<-aov(soilmoisture~wateringlevel+Error(block/wateringlevel)) >> >> I then plotted (soilmoisture~wateringlevel) and fitted a regression line: >> lm1<-lm(soilmoisture~wateringlevel) >> abline(lm1,lty=1) >> >> >> Here are my questions: >> >> 1) Is the repeated measures anova I have entered correct to tell me if >> there is a significant difference in my watering levels? > > It seems like using anova here is reasonable -- I haven't used it in R, > though, so I can't comment on your *actual* use of it (looks right -- can't > u also just call aov on your lm1 object, too?) > >> 2) How do I calculate r2 value to show much variation my watering >> level explains? - and then put this figure on my plot? > > I think this should be pretty straight forward -- you can find the formula > here: > > http://en.wikipedia.org/wiki/Fraction_of_variance_unexplained > > and R^2 = SSR / SST (on that page) > > Using the formulas on that page, translating to R is pretty straight > forward. Let's take the example code at the bottom of ?lm help page for > reference and calculate the R^2: > > ssr <- sum((predict(lm.D9, group) - weight)^2) > sst <- sum((weight - mean(weight))^2) > r2 <- ssr / sst > > HTH, > -steve > > -- > Steve Lianoglou > Graduate Student: Computational Systems Biology > | Memorial Sloan-Kettering Cancer Center > | Weill Medical College of Cornell University > Contact Info: http://cbio.mskcc.org/~lianos/contact > > ______________________________________________ > 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. > -- HUANG Ronggui, Wincent PhD Candidate Dept of Public and Social Administration City University of Hong Kong Home page: http://asrr.r-forge.r-project.org/rghuang.html ______________________________________________ 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.
