getwd()
## [1] "C:/Users/skirmantas/OneDrive/Desktop/Duomenys"
setwd("C:/Users/skirmantas/OneDrive/Desktop")
duom <- read.csv2("C:/Users/skirmantas/OneDrive/Desktop/Duomenys/DPP1.csv", header = TRUE, sep = ";", dec = ".")
library(stats)
pc.cr <- princomp(duom, cor = TRUE)
pc.cr
## Call:
## princomp(x = duom, cor = TRUE)
##
## Standard deviations:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7
## 2.1826394 1.1339612 0.8311506 0.4168373 0.2007666 0.1815120 0.1113495
##
## 7 variables and 53940 observations.
plot(pc.cr$scores[,1], pc.cr$scores[,2], pch=18)
op = par(cex.main=0.53, cex.axis=1, cex.lab=0.7, cex=0.83)
biplot(pc.cr)
pc <- prcomp(duom, center = TRUE, scale = TRUE)
prcomp(duom, scale = FALSE)
## Standard deviations (1, .., p=7):
## [1] 3.989440e+03 2.283217e+00 1.335150e+00 8.202770e-01 1.948531e-01
## [6] 1.256807e-01 7.795305e-02
##
## Rotation (n x k) = (7 x 7):
## PC1 PC2 PC3
## Carat.Weight.of.Daimond. 1.095002e-04 -1.268053e-02 -3.095226e-02
## Depth -3.823523e-06 2.855246e-01 -9.546313e-01
## Table 7.120789e-05 -9.562977e-01 -2.805972e-01
## Price.in.US.dollars. 9.999999e-01 9.340588e-05 4.969435e-05
## X.length. 2.486877e-04 -4.450580e-02 -3.885860e-02
## Y.width. 2.477609e-04 -4.173598e-02 -3.277768e-02
## Z.Depth. 1.523479e-04 -9.328059e-03 -8.001227e-02
## PC4 PC5 PC6
## Carat.Weight.of.Daimond. -0.1881334278 1.693347e-01 -2.727369e-01
## Depth 0.0616683380 -3.426591e-02 -4.147808e-02
## Table 0.0817901805 -6.533849e-03 3.927639e-03
## Price.in.US.dollars. 0.0003884624 -1.965194e-05 1.540157e-05
## X.length. -0.6063073272 4.761949e-01 -5.189888e-01
## Y.width. -0.6626667125 -7.420649e-01 8.266921e-02
## Z.Depth. -0.3838975099 4.389613e-01 8.047951e-01
## PC7
## Carat.Weight.of.Daimond. 0.9275922119
## Depth -0.0213840864
## Table -0.0034998183
## Price.in.US.dollars. -0.0000282091
## X.length. -0.3644031583
## Y.width. 0.0237073198
## Z.Depth. 0.0758383874
princomp(duom, cor = FALSE, scores = TRUE)
## Call:
## princomp(x = duom, cor = FALSE, scores = TRUE)
##
## Standard deviations:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6
## 3.989403e+03 2.283196e+00 1.335138e+00 8.202694e-01 1.948513e-01 1.256795e-01
## Comp.7
## 7.795232e-02
##
## 7 variables and 53940 observations.
#install.packages("factoextra")
library(factoextra)
## Įkeliamas reikalingas paketas: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
res.pca <- prcomp(duom, scale = TRUE)
fviz_eig(res.pca)
fviz_pca_ind(res.pca,
col.ind = "cos2", # Color by the quality of representation
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Avoid text overlapping
)
## Warning: ggrepel: 53939 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
fviz_pca_var(res.pca,
col.var = "contrib", # Color by contributions to the PC
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Avoid text overlapping
)
fviz_pca_biplot(res.pca, repel = TRUE,
col.var = "#2E9FDF", # Variables color
col.ind = "#696969" # Individuals color
)
## Warning: ggrepel: 53939 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
