022-33574735 / 9923170071 / 8108094992 info@dimensionless.in



We will apply pca on wine dataset

wine = read.csv
("https://storage.googleapis.
com/dimensionless/
Analytics/wine.csv")

Applying PCA on relevant predictors

pca<-prcomp(wine[,3:7],scale=TRUE)

Analyzing components of the output

#Std Dev
pca$sdev
## [1] 1.45691795 1.16557440 0.99526725 0.72328569 0.07160523
# Loadings
pca$rotation
##                     PC1        PC2        PC3         PC4         PC5
## WinterRain   0.09395915  0.7384046 -0.1256430 -0.65563602  0.01689675
## AGST        -0.32836427 -0.3806578  0.6264975 -0.59544647  0.01486508
## HarvestRain  0.03679770 -0.5244412 -0.7238807 -0.44675373 -0.00390888
## Age         -0.66342357  0.1258942 -0.1914225  0.10156506  0.70502609
## FrancePop    0.66472828 -0.1377328  0.1762640 -0.07536942  0.70881341
# Principal Components
pca$x
##               PC1         PC2         PC3         PC4          PC5
##  [1,] -2.66441523  0.01812071 -0.19940771 -0.26187403  0.017848626
##  [2,] -2.31090775  1.27230388  0.17749206  0.09070174 -0.006316369
##  [3,] -2.31872688 -0.42425903  0.34077385  0.31372038 -0.067315308
##  [4,] -1.55060520 -0.23588712 -0.23518124  1.69094289 -0.101731306
##  [5,] -1.35803408 -0.06913418 -0.82614968  0.15237445 -0.073508609
##  [6,] -1.77313036 -1.24596188  0.30308288 -0.33015372 -0.062254812
##  [7,] -0.83734190  0.14770821 -1.90545030 -1.40861601 -0.059226672
##  [8,] -1.17507833  1.74417439  1.38340778 -1.06038701 -0.003711288
##  [9,] -0.49978424  1.43298732  0.48615479  0.39280758  0.049944991
## [10,] -0.01341322  0.49601115 -0.91321708  0.70204963  0.066036711
## [11,] -0.75505205 -1.14907041  1.34584178  0.68608150  0.093179804
## [12,]  0.56223704 -0.19991293 -2.22360713  0.32097131  0.062303660
## [13,]  0.22813081  1.59605527  0.45968547 -0.71903876  0.121180565
## [14,]  0.47318950  0.92227025  0.01377674 -0.14755601  0.084300103
## [15,]  0.65743468 -0.89650446 -1.56747979 -0.66837607  0.043747752
## [16,]  0.60397262 -0.98362933 -0.69683131 -0.53748100  0.042134220
## [17,]  0.67149628  0.27205617  0.92090308  0.03475269  0.053849458
## [18,]  0.76315093 -0.37837929  0.90694860  0.13667046  0.053372925
## [19,]  1.81242805  0.18510809 -1.13339807  1.48444569  0.007580131
## [20,]  0.83436088 -1.66846501  1.33756198  0.62859729  0.028001330
## [21,]  1.52887804 -0.59071652 -0.11300095 -0.06358380  0.010558586
## [22,]  1.33939957 -0.90295396  0.65594023 -0.56734753 -0.015034228
## [23,]  1.05051137 -2.71675250  0.74697721 -0.89482443 -0.075496028
## [24,]  2.38846524  1.80061406  0.03888058 -0.12744556 -0.110346034
## [25,]  2.34283421  1.57421714  0.69629625  0.15256833 -0.159098209

Creating biplot

biplot(pca,scale=0)

Calculating proportion of variance

pr.var<-pca$sdev^2
pve<-pr.var/sum(pr.var)

Creating scree plot and cumulative plots

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")


Building model using PC1 to PC4

predictor<-pca$x[,1:4]
wine<-cbind(wine,predictor)
model<-lm(Price~PC1+PC2+PC3+PC4,data=wine)
summary(model)
## 
## Call:
## lm(formula = Price ~ PC1 + PC2 + PC3 + PC4, data = wine)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.46899 -0.24789 -0.00215  0.20607  0.52709 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  7.06722    0.05889 120.016  < 2e-16 ***
## PC1         -0.25487    0.04125  -6.178 4.91e-06 ***
## PC2          0.12730    0.05156   2.469   0.0227 *  
## PC3          0.41744    0.06039   6.913 1.03e-06 ***
## PC4         -0.18647    0.08309  -2.244   0.0363 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2944 on 20 degrees of freedom
## Multiple R-squared:  0.8292, Adjusted R-squared:  0.795 
## F-statistic: 24.27 on 4 and 20 DF,  p-value: 1.964e-07

Making Predictions

We cannot convert test data into principal components, by applying pca. Instead we have to apply same transformations on test data as we did for train data

wineTest = read.csv("https://storage.googleapis.com/dimensionless/Analytics/wine_test.csv")
wineTest
##   Year  Price WinterRain    AGST HarvestRain Age FrancePop
## 1 1979 6.9541        717 16.1667         122   4  54835.83
## 2 1980 6.4979        578 16.0000          74   3  55110.24
pca_test<-predict(pca,wineTest[,3:7])
class(pca_test)
## [1] "matrix"
pca_test
##           PC1       PC2       PC3        PC4        PC5
## [1,] 2.303725 0.5946824 0.4101509 -0.3722356 -0.2074747
## [2,] 2.398317 0.2242893 0.8925278  0.7329912 -0.2649691
# Converting to data frame
pca_test<-as.data.frame(pca_test)
pca_test
##        PC1       PC2       PC3        PC4        PC5
## 1 2.303725 0.5946824 0.4101509 -0.3722356 -0.2074747
## 2 2.398317 0.2242893 0.8925278  0.7329912 -0.2649691

Making predictions

pred_pca<-predict(object = model, newdata=pca_test)
pred_pca
##        1        2 
## 6.796398 6.720412
wineTest$Price
## [1] 6.9541 6.4979

 

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