# Principal Component Analysis in R

Principal Component Analysis in R, PCA is used in exploratory data analysis and for making decisions in predictive models.

PCA is commonly used for dimensionality reduction by using each data point onto only the first few principal components (most cases first and second dimensions) to obtain lower-dimensional data while keeping as much of the data’s variation as possible.

## Principal Component Analysis in R

The first principal component can equivalently be defined as a direction that maximizes the variance of the projected data.

The principal components are often analyzed by eigendecomposition of the data covariance matrix or singular value decomposition (SVD) of the data matrix.

Reducing the number of variables from a data set naturally leads to inaccuracy, but the trick in dimensionality reduction is to allow us to make correct decisions based on high accuracy.

Always smaller data sets are easier to explore, visualize, analyze, and faster for machine learning algorithms.

We’ll utilize the iris dataset in R to analyze and interpret data in this session.

### Getting Data

data("iris") str(iris)

data. frame’: 150 obs. of 5 variables:

$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

This dataset contains 150 observations with 5 variables.

summary(iris)

Sepal.Length Sepal.Width Petal.Length Petal.Width Species Min. :4.3 Min. :2.0 Min. :1.0 Min. :0.1 setosa :50 1st Qu.:5.1 1st Qu.:2.8 1st Qu.:1.6 1st Qu.:0.3 versicolor:50 Median :5.8 Median :3.0 Median :4.3 Median :1.3 virginica :50 Mean :5.8 Mean :3.1 Mean :3.8 Mean :1.2 3rd Qu.:6.4 3rd Qu.:3.3 3rd Qu.:5.1 3rd Qu.:1.8 Max. :7.9 Max. :4.4 Max. :6.9 Max. :2.5

**Partition Data**

Let’s divide the data sets into training datasets and test datasets.

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set.seed(111) ind <- sample(2, nrow(iris), replace = TRUE, prob = c(0.8, 0.2)) training <- iris[ind==1,] testing <- iris[ind==2,]

**Scatter Plot & Correlations**

library(psych)

We’ll start by looking at the correlation between the independent variables. For correlation data analysis, we’ll remove the factor variable from the dataset.

pairs.panels(training[,-5], gap = 0, bg = c("red", "yellow", "blue")[training$Species], pch=21)

Lower triangles provide scatter plots and upper triangles provide correlation values.

Petal length and petal width are highly correlated. Same way sepal length and petal length, Sepeal length, and petal width are also highly correlated.

This leads to multicollinearity issues. So if we predict the model based on this dataset may be erroneous.

One way of handling these kinds of issues is based on PCA.

**Principal Component Analysis in R**

Only independent variables are used in Principal Component Analysis. As a result, the fifth variable was eliminated from the dataset.

pc <- prcomp(training[,-5], center = TRUE, scale. = TRUE) attributes(pc)

$names

[1] "sdev" "rotation" "center" [4] "scale" "x" $class [1] "prcomp"

pc$center Sepal.Length Sepal.Width Petal.Length 5.8 3.1 3.6 Petal.Width 1.1

Scale function is used for normalization

pc$scale Sepal.Length Sepal.Width Petal.Length 0.82 0.46 1.79 Petal.Width 0.76

Print the results stored on pc.

print(pc)

while printing pc you will get standard deviations and loadings.

Standard deviations (1, .., p=4): [1] 1.72 0.94 0.38 0.14 Rotation (n x k) = (4 x 4): PC1 PC2 PC3 PC4 Sepal.Length 0.51 -0.398 0.72 0.23 Sepal.Width -0.29 -0.913 -0.26 -0.12 Petal.Length 0.58 -0.029 -0.18 -0.80 Petal.Width 0.56 -0.081 -0.62 0.55

For example, PC1 increases when Sepal Length, Petal Length, and Petal Width are increased and it is positively correlated whereas PC1 increases Sepal Width decrease because these values are negatively correlated.

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summary(pc) Importance of components: PC1 PC2 PC3 Standard deviation 1.717 0.940 0.3843 Proportion of Variance 0.737 0.221 0.0369 Cumulative Proportion 0.737 0.958 0.9953 PC4 Standard deviation 0.1371 Proportion of Variance 0.0047 Cumulative Proportion 1.0000

The first principal components explain the variability around 73% and its captures the majority of the variability.

In this case, the first two components capture the majority of the variability.

**Orthogonality of PCs**

Let’s create the scatterplot based on PC and see the multicollinearity issue is addressed or not?.

pairs.panels(pc$x, gap=0, bg = c("red", "yellow", "blue")[training$Species], pch=21)

Now the correlation coefficients are zero, so we can get rid of multicollinearity issues.

**Bi-Plot**

For making biplot need devtools package.

library(devtools) #install_github("vqv/ggbiplot") library(ggbiplot) g <- ggbiplot(pc, obs.scale = 1, var.scale = 1, groups = training$Species, ellipse = TRUE, circle = TRUE, ellipse.prob = 0.68) g <- g + scale_color_discrete(name = '') g <- g + theme(legend.direction = 'horizontal', legend.position = 'top') print(g)

PC1 explains about 73.7% and PC2 explained about 22.1% of the variability.

Arrows are closer to each other indicates a high correlation.

For example correlation between Sepal Width vs other variables is weakly correlated.

Another way interpreting the plot is PC1 is positively correlated with the variables Petal Length, Petal Width, and Sepal Length, and PC1 is negatively correlated with Sepal Width.

PC2 is highly negatively correlated with Sepal Width.

Bi plot is an important tool in PCA to understand what is going on in the dataset.

Difference between association and correlation

## Prediction with Principal Components

trg <- predict(pc, training)

Add the species column information into trg.

trg <- data.frame(trg, training[5]) tst <- predict(pc, testing) tst <- data.frame(tst, testing[5])

Multinomial Logistic regression with First Two PCs

Because our dependent variable has 3 levels, so we will make use of multinomial logistic regression.

library(nnet) trg$Species <- relevel(trg$Species, ref = "setosa") mymodel <- multinom(Species~PC1+PC2, data = trg) summary(mymodel)

Model out is given below and we used only the first two principal components because the majority of the information’s available in the first components.

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multinom(formula = Species ~ PC1 + PC2, data = trg) Coefficients: (Intercept) PC1 PC2 versicolor 7.23 14 3.2 virginica -0.58 20 3.6 Std. Errors: (Intercept) PC1 PC2 versicolor 188 106 128 virginica 188 106 128 Residual Deviance: 36 AIC: 48

### Confusion Matrix & Misclassification Error – training

p <- predict(mymodel, trg) tab <- table(p, trg$Species) tab

Let’s see the correct classification and miss classifications in the training dataset.

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p setosa versicolor virginica setosa 45 0 0 versicolor 0 35 3 virginica 0 5 32

1 - sum(diag(tab))/sum(tab)

Misclassification error is 0.067

### Confusion Matrix & Misclassification Error – testing

p1 <- predict(mymodel, tst) tab1 <- table(p1, tst$Species) tab1

Based on the test data set error classification is calculated.

p1 setosa versicolor virginica setosa 5 0 0 versicolor 0 9 3 virginica 0 1 12 1 - sum(diag(tab1))/sum(tab1) 0.13

Misclassification for the testing data set is 13.33%, indicating 86.67% model accuracy.