Lab 1: Principal Components Analysis

states=row.names(USArrests)
states
 [1] "Alabama"        "Alaska"         "Arizona"        "Arkansas"       "California"     "Colorado"       "Connecticut"   
 [8] "Delaware"       "Florida"        "Georgia"        "Hawaii"         "Idaho"          "Illinois"       "Indiana"       
[15] "Iowa"           "Kansas"         "Kentucky"       "Louisiana"      "Maine"          "Maryland"       "Massachusetts" 
[22] "Michigan"       "Minnesota"      "Mississippi"    "Missouri"       "Montana"        "Nebraska"       "Nevada"        
[29] "New Hampshire"  "New Jersey"     "New Mexico"     "New York"       "North Carolina" "North Dakota"   "Ohio"          
[36] "Oklahoma"       "Oregon"         "Pennsylvania"   "Rhode Island"   "South Carolina" "South Dakota"   "Tennessee"     
[43] "Texas"          "Utah"           "Vermont"        "Virginia"       "Washington"     "West Virginia"  "Wisconsin"     
[50] "Wyoming"       
names(USArrests)
[1] "Murder"   "Assault"  "UrbanPop" "Rape"    
apply(USArrests,2, mean)
  Murder  Assault UrbanPop     Rape 
   7.788  170.760   65.540   21.232 
apply(USArrests,2, var)
    Murder    Assault   UrbanPop       Rape 
  18.97047 6945.16571  209.51878   87.72916 
pr.out = prcomp (USArrests, scale =TRUE)
names(pr.out )
[1] "sdev"     "rotation" "center"   "scale"    "x"       
pr.out$center
  Murder  Assault UrbanPop     Rape 
   7.788  170.760   65.540   21.232 
pr.out$scale
   Murder   Assault  UrbanPop      Rape 
 4.355510 83.337661 14.474763  9.366385 
pr.out$rotation
                PC1        PC2        PC3         PC4
Murder   -0.5358995  0.4181809 -0.3412327  0.64922780
Assault  -0.5831836  0.1879856 -0.2681484 -0.74340748
UrbanPop -0.2781909 -0.8728062 -0.3780158  0.13387773
Rape     -0.5434321 -0.1673186  0.8177779  0.08902432
dim(pr.out$x )
[1] 50  4
biplot (pr.out , scale =0)

pr.out$rotation=-pr.out$rotation
pr.out$x=-pr.out$x
biplot (pr.out , scale =0)

pr.out$sdev
[1] 1.5748783 0.9948694 0.5971291 0.4164494
pr.var =pr.out$sdev ^2
pr.var
[1] 2.4802416 0.9897652 0.3565632 0.1734301
pve=pr.var/sum(pr.var )
pve
[1] 0.62006039 0.24744129 0.08914080 0.04335752
plot(pve , xlab="Principal Component", ylab="Proportion of
Variance Explained", ylim=c(0,1) ,type="b")

plot(cumsum (pve ), xlab="Principal Component", ylab = "Cumulative Proportion of Variance Explained", ylim=c(0,1) ,
type="b")

a=c(1,2,8,-3)
cumsum (a)
[1]  1  3 11  8
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