On 3/12/2016 12:39 PM, Axel wrote:
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.
There are already plenty of tools for this; don't bother trying to re-invent an already well-working wheel.
* PCA + a biplot will give you a good overview. With groups, I recommend ggbiplot, with data ellipses for the groups.
This shows clear separation along PC1 data(olive, package="tourr") library(ggbiplot) olivenum <- olive[,c(3:10)] olive.pca <- prcomp(olivenum, scale.=TRUE) summary(olive.pca) # region should be a factor (area has 9 levels, maybe too confusing) olive$region <- factor(olive$region, labels=c("North", "Sardinia", "South")) ggbiplot(olive.pca, obs.scale = 1, var.scale = 1, groups = olive$region, ellipse = TRUE, varname.size=4, circle = TRUE) + theme_bw() + theme(legend.direction = 'horizontal', legend.position = 'top') * Discrimination among regions by chemical composition: A canonical discriminant analysis will show you this in a low-rank view. The biggest difference is between the North vs. the other 2. # MLM olive.mlm <- lm(as.matrix(olive[,c(3:10)]) ~ olive$region, data=olive) # Canonical discriminant analysis # (need devel. version for ellipses) # install.packages("candisc", repos="http://R-Forge.R-project.org") library(candisc) olive.can <- candisc(olive.mlm) olive.can plot(olive.can, ellipse=TRUE) * You can probably use the predict() method for MASS::lda() to predict the class for new samples. hope this helps, -Michael ______________________________________________ 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.