Multivariate Probability Distributions in R
Surajit Ray
Professor, University of Glasgow
princomp()
Zjednodušený formát
princomp(x, cor = FALSE, scores = TRUE)
x: numerická matice nebo datový rámeccor: použití korelační matice místo kovariančníscores: vypočítají se skóry/projekce dat na hlavní komponentyDatová sada mtcars obsahuje 11 proměnných spotřeby paliva pro 32 automobilů
head(mtcars,5)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
vs a am – obě jsou binárnímtcars.sub <- mtcars[ , -c(8,9)]
$$cars.pca <- princomp(mtcars.sub, cor = TRUE, scores = TRUE)
cars.pca
Standard deviations:
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9
2.378 1.443 0.710 0.515 0.428 0.352 0.324 0.242 0.149
summary(cars.pca)
Importance of components:
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9
Standard deviation 2.378 1.443 0.710 0.5148 0.4280 0.3518 0.3241 0.2419 0.14896
Proportion of Variance 0.628 0.231 0.056 0.0294 0.0204 0.0138 0.0117 0.0065 0.00247
Cumulative Proportion 0.628 0.860 0.916 0.9453 0.9656 0.9794 0.9910 0.9975 1.00000
Multivariate Probability Distributions in R