Or with plyr there's a more flexible approach: res.aov <- dlply(warpbreaks, .(tension), function(x) aov(breaks ~ wool, data=x)) # aov results are stored in a list, you can directly extract what you want with l*ply
l_ply(res.aov, function(x) print(summary(x))) ldply(res.aov, function(x) data.frame( fitted(x))) ldply(res.aov, coefficients) HTH, Xavier ----- Mail Original ----- De: "Tobias Verbeke" <[email protected]> À: "baxterj" <[email protected]> Cc: [email protected] Envoyé: Lundi 28 Septembre 2009 22h55:34 GMT +01:00 Amsterdam / Berlin / Berne / Rome / Stockholm / Vienne Objet: Re: [R] Running an ANOVA with a BY baxterj wrote: > I downloaded the package and got it to work with the coding: > > model <- function(df) {aov(values ~ WellID, data = twelldata)} Hmm.. I guess you mean to use 'data = df' instead of 'data = twelldata' > ANOVA1way <- dlply(twelldata, .(Analyte), model) > print(ANOVA1way) > > This gives me degrees of freedom and sum of squares for each anova per > analyte. However, I cant get the summary(ANOVA1way) to work so that I can > get p-values, etc... How can I do this? You need to extend your model function: - extract the bits you want - construct a result data frame - return it and use a ddply For example (non-tested): model <- function(df) { lmObj <- lm(values ~ WellID, data = df) summaryLmObj <- summary(lmObj) res <- data.frame(intercept = coef(lmObj)[1], adjr2 = summaryLmObj$adj.r.squared) # extract and insert anything return(res) } (ANOVA1way <- ddply(twelldata, .(Analyte), model)) HTH, Tobias > Tobias Verbeke-2 wrote: >> baxterj wrote: >>> I have a simple 1 way anova coded like >>> >>> summary(ANOVA1way <- aov(Value ~ WellID, data = welldata)) >>> >>> How can I use the BY function to do this ANOVA for each group using >>> another >>> variable in the dataset?? I tried coding it like this, but it doesn't >>> seem >>> to work. >>> >>> summary(ANOVA1way <- by(welldata, Analyte, function(x) aov(Value ~ >>> WellID, >>> data = welldata))) >>> >>> In SAS I would code it like this: >>> Proc sort data=welldata; by analyte; run; >>> Proc glm data=welldata; >>> by analyte; >>> class wellid; >>> model value = wellid; >>> run; >> Look at the plyr package for a general solution to >> this type of problems: >> >> http://cran.r-project.org/web/packages/plyr/index.html >> >> and its introductory guide on the package home page: >> >> http://had.co.nz/plyr/ >> >> HTH, >> Tobias >> >> ______________________________________________ >> [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 >> and provide commented, minimal, self-contained, reproducible code. >> >> > ______________________________________________ [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 and provide commented, minimal, self-contained, reproducible code. ______________________________________________ [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 and provide commented, minimal, self-contained, reproducible code.
