# Two factors -> nurical data dependency analyzing

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## Two factors -> nurical data dependency analyzing

 Hello, dear R users. What is the easiest and the most visualli understandable way to analize dependency of numerical variable on two factors? Is the boxplot(y~f1+f2) the good way? It seems that this formula does not work. -- Evgeniy ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
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## Re: Two factors -> nurical data dependency analyzing

 On Sun, 19 Feb 2006, Evgeniy Kachalin wrote: > Hello, dear R users. > > What is the easiest and the most visualli understandable way to analize > dependency of numerical variable on two factors? interaction.plot() is a good start. > Is the > boxplot(y~f1+f2) the good way? It seems that this formula does not work. No, nor is it documented to: the help page is there to help you.  You need a single factor as the grouping, so make one via an interaction. boxplot(y ~ f1:f2) should work.  E.g. library(MASS) boxplot(FL ~ sex:sp, data=crabs) Another idea is to use lattice's bwplot.  E.g. library(lattice) bwplot(FL ~ sex | sp, data=crabs) -- Brian D. Ripley,                  [hidden email] Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/University of Oxford,             Tel:  +44 1865 272861 (self) 1 South Parks Road,                     +44 1865 272866 (PA) Oxford OX1 3TG, UK                Fax:  +44 1865 272595 ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
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## Re: Two factors -> nurical data dependency analyzing

 Prof Brian Ripley ?????: > On Sun, 19 Feb 2006, Evgeniy Kachalin wrote: > >> Hello, dear R users. >> >> What is the easiest and the most visualli understandable way to analize >> dependency of numerical variable on two factors? > > interaction.plot() is a good start. > >> Is the >> boxplot(y~f1+f2) the good way? It seems that this formula does not work. > > No, nor is it documented to: the help page is there to help you. You > need a single factor as the grouping, so make one via an interaction. > boxplot(y ~ f1:f2) should work. E.g. > > library(MASS) > boxplot(FL ~ sex:sp, data=crabs) Does not work: Îøèáêà â if (any(out[nna])) stats[c(1, 5)] <- range(x[!out], na.rm = TRUE) : ïðîïóùåííîå çíà÷åíèå, à íóæíî TRUE/FALSE Âäîáàâîê: Warning messages: 1: + not meaningful for factors in: Ops.factor(x[floor(d)], x[ceiling(d)]) 2: < not meaningful for factors in: Ops.factor(x, (stats[2] - coef * iqr)) 3: > not meaningful for factors in: Ops.factor(x, (stats[4] + coef * iqr)) Hm... > Another idea is to use lattice's bwplot. E.g. > > library(lattice) > bwplot(FL ~ sex | sp, data=crabs) > > That's not the point. The scales may differ significantly, also this is not conviniet for many factors. ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
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## Re: Two factors -> nurical data dependency analyzing

 I carefully tested my suggestions in the current version of R, 2.2.1, before posting.  They DO work, and as you have not even told us your version of R, we have no idea what you have broken on your R installation. On Sun, 19 Feb 2006, Evgeniy Kachalin wrote: > Prof Brian Ripley ?????: >> On Sun, 19 Feb 2006, Evgeniy Kachalin wrote: >> >>> Hello, dear R users. >>> >>> What is the easiest and the most visualli understandable way to analize >>> dependency of numerical variable on two factors? >> >> interaction.plot() is a good start. >> >>> Is the >>> boxplot(y~f1+f2) the good way? It seems that this formula does not work. >> >> No, nor is it documented to: the help page is there to help you. You need a >> single factor as the grouping, so make one via an interaction. >> boxplot(y ~ f1:f2) should work. E.g. >> >> library(MASS) >> boxplot(FL ~ sex:sp, data=crabs) > Does not work: > Îøèáêà â if (any(out[nna])) stats[c(1, 5)] <- range(x[!out], na.rm = TRUE) : > ïðîïóùåííîå çíà÷åíèå, à íóæíî TRUE/FALSE > Âäîáàâîê: Warning messages: > 1: + not meaningful for factors in: Ops.factor(x[floor(d)], x[ceiling(d)]) > 2: < not meaningful for factors in: Ops.factor(x, (stats[2] - coef * iqr)) > 3: > not meaningful for factors in: Ops.factor(x, (stats[4] + coef * iqr)) > > Hm... > >> Another idea is to use lattice's bwplot. E.g. >> >> library(lattice) >> bwplot(FL ~ sex | sp, data=crabs) >> >> > That's not the point. The scales may differ significantly, also this is not > conviniet for many factors. > > -- Brian D. Ripley,                  [hidden email] Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/University of Oxford,             Tel:  +44 1865 272861 (self) 1 South Parks Road,                     +44 1865 272866 (PA) Oxford OX1 3TG, UK                Fax:  +44 1865 272595______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide! http://www.R-project.org/posting-guide.html