R usefull for qsar

library(pls)
qsar<-data.frame(K<-c(K1, K2, ...))
qsar$x<-as.matrix(read.table("~/qsar.csv", sep=","))
qsarm<-plsr(K~x, 2, data=qsar)
summary(qsarm)
plot(qsarm$loadings)


qsar<-data.frame(K<-c(0.2, 235, 1.6, 3.9, 660, 314, 2, 91, 3.9))
qsar$x<-as.matrix(read.table("C:/qsar3_small.csv", sep=","))
library(rgl)
library(scatterplot3d)
s3d <- scatterplot3d(K, qsar$x[,2], qsar$x[,3])
s3d$plane3d(fit)

library(MASS)
step <- stepAIC(fitl, direction="both")
#isprastina nereikalingus xsus is fito


#dar geriau...
library(leaps)
leaps<-regsubsets(K~qsar$x, data=qsar)
plot(leaps, scale="r2")
#rodo juodais laukeliais ka imant kiek R^2 liks dar