Inline. Cheers, Bert Bert Gunter
"The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Sat, Mar 12, 2016 at 9:39 AM, Axel <axeldib...@alice.it> wrote: > Hi to all the members of the list! > > I am a novice as regards to statistical > analysis and the use of the R software, so I am experimenting with the dataset > "olive" included in the package "tourr". Stop experimenting and spend time with an R tutorial or two? There are many good ones on the Web. See also https://www.rstudio.com/online-learning/#R for some recommendations. > This dataset contains the results of > the determination of the fatty acids in 572 samples of olive oil from Italy > (columns from 3 to 10) along with the area and the region of origin of the oil > (respectively, column 1 and column 2). > > The main goal of my analysis is to > determine which are the fatty acids that characterize the origin of an oil. As > a secondary goal, I wolud like to insert the results of the chemical analysis > of an oil that I analyzed (I am a Chemistry student) in order to determine its > region of production. I do not know if this last thing is possibile. > > I am > using R 3.2.4 on MacOS X El Capitan with the packages "tourr" and "psych" > loaded. > Here are the commands I have used up to now: > > olivenum <- olive[,c(3: > 10)] > mean <- colMeans(olivenum) > sd <- sapply(olivenum,sd) > describeBy(olivenum, > olive[2]) > pairs(olivenum) > R <- cor(olivenum) > eigen(R) > # Since the first three > autovalues are greater than 1, these are the main components (column 1, 2 and > 3). But I can determine them also using a scree diagram as following. Right? > > autoval <- eigen(R)$values > autovec <- eigen(R)$vectors > pvarsp <- autoval/ncol > (olivenum) > plot(autoval,type="b",main="Scree diagram",xlab="Number of > components",ylab="Autovalues") > abline(h=1,lwd=3,col="red") > > eigen (R)$vectors[, > 1:3] > olive.scale <- scale(olivenum,T,T) > points <- olive.scale%*%autovec[,1:3] > > > #Since I selected three main components (three columns), how should I plot the > dispersion graph? I do not think that what I have done is right: > plot(points, > main="Dispersion graph",xlab="Component 1",ylab="Component 2") > princomp > (olivenum,cor=T) > #With the following command I obtain a summary of the > importance of components. For example, the variance of component 1 is about > 0,465, of component 2 is 0,220 and of component 3 is 0,127 with a cumulative > variance of 0,812. This means that the values in the first three columns of > the > matrix "olivenum" mostly characterize the differences between the > observations. > Right? > summary(princomp(olivenum,cor=T)) > screeplot(princomp(olivenum,cor=T)) > > plot(princomp(olivenum,cor=T)$scores,rownames(olivenum)) > abline(h=0,v=0) > > I > determined that three components can explain a great part of variability but I > don't know which are these components. How should I continue? > > Thank you for > > attention, > Axel > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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 -- To UNSUBSCRIBE and more, see 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.
