Computing and visualizing LDA in R

As I have described before, Linear Discriminant Analysis (LDA) can be seen from two different angles. The first classify a given sample of predictors {x} to the class {C_l} with highest posterior probability {\pi(y = C_l|x)}. It minimizes the total probability of misclassification. To compute {\pi(y = C_l|x)} it uses Bayes’ rule and assume that {\pi(x|y = C_l)} follows a Gaussian distribution with class-specific mean {\mu_l} and common covariance matrix {\Sigma}. The second tries to find a linear combination of the predictors that gives maximum separation between the centers of the data while at the same time minimizing the variation within each group of data.

The second approach [1] is usually preferred in practice due to its dimension-reduction property and is implemented in many R packages, as in the lda function of the MASS package for example. In what follows, I will show how to use the lda function and visually illustrate the difference between Principal Component Analysis (PCA) and LDA when applied to the same dataset.

Using lda from MASS R package

As usual, we are going to illustrate lda using the iris dataset. The data contains four continuous variables which correspond to physical measures of flowers and a categorical variable describing the flowers’ species.

require(MASS)

# Load data
data(iris)

> head(iris, 3)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa

An usual call to lda contains formula, data and prior arguments [2].

r <- lda(formula = Species ~ ., 
         data = iris, 
         prior = c(1,1,1)/3)

The . in the formula argument means that we use all the remaining variables in data as covariates. The prior argument sets the prior probabilities of class membership. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels.

> r$prior
   setosa versicolor  virginica 
0.3333333  0.3333333  0.3333333 

> r$counts
setosa versicolor  virginica 
50         50         50 

> r$means
           Sepal.Length Sepal.Width Petal.Length Petal.Width
setosa            5.006       3.428        1.462       0.246
versicolor        5.936       2.770        4.260       1.326
virginica         6.588       2.974        5.552       2.026

> r$scaling
                    LD1         LD2
Sepal.Length  0.8293776  0.02410215
Sepal.Width   1.5344731  2.16452123
Petal.Length -2.2012117 -0.93192121
Petal.Width  -2.8104603  2.83918785

> r$svd
[1] 48.642644  4.579983

As we can see above, a call to lda returns the prior probability of each class, the counts for each class in the data, the class-specific means for each covariate, the linear combination coefficients (scaling) for each linear discriminant (remember that in this case with 3 classes we have at most two linear discriminants) and the singular values (svd) that gives the ratio of the between- and within-group standard deviations on the linear discriminant variables.

prop = r$svd^2/sum(r$svd^2)

> prop
[1] 0.991212605 0.008787395

We can use the singular values to compute the amount of the between-group variance that is explained by each linear discriminant. In our example we see that the first linear discriminant explains more than {99\%} of the between-group variance in the iris dataset.

If we call lda with CV = TRUE it uses a leave-one-out cross-validation and returns a named list with components:

  • class: the Maximum a Posteriori Probability (MAP) classification (a factor)
  • posterior: posterior probabilities for the classes.
r2 <- lda(formula = Species ~ ., 
          data = iris, 
          prior = c(1,1,1)/3,
          CV = TRUE)

> head(r2$class)
[1] setosa setosa setosa setosa setosa setosa
Levels: setosa versicolor virginica

> head(r2$posterior, 3)
  setosa   versicolor    virginica
1      1 5.087494e-22 4.385241e-42
2      1 9.588256e-18 8.888069e-37
3      1 1.983745e-19 8.606982e-39

There is also a predict method implemented for lda objects. It returns the classification and the posterior probabilities of the new data based on the Linear Discriminant model. Below, I use half of the dataset to train the model and the other half is used for predictions.

train <- sample(1:150, 75)

r3 <- lda(Species ~ ., # training model
         iris, 
         prior = c(1,1,1)/3, 
         subset = train)

plda = predict(object = r, # predictions
               newdata = iris[-train, ])

> head(plda$class) # classification result
[1] setosa setosa setosa setosa setosa setosa
Levels: setosa versicolor virginica

> head(plda$posterior, 3) # posterior prob.
  setosa   versicolor    virginica
3      1 1.463849e-19 4.675932e-39
4      1 1.268536e-16 3.566610e-35
5      1 1.637387e-22 1.082605e-42

> head(plda$x, 3) # LD projections
       LD1        LD2
3 7.489828 -0.2653845
4 6.813201 -0.6706311
5 8.132309  0.5144625

Visualizing the difference between PCA and LDA

As I have mentioned at the end of my post about Reduced-rank DA, PCA is an unsupervised learning technique (don’t use class information) while LDA is a supervised technique (uses class information), but both provide the possibility of dimensionality reduction, which is very useful for visualization. Therefore we would expect (by definition) LDA to provide better data separation when compared to PCA, and this is exactly what we see at the Figure below when both LDA (upper panel) and PCA (lower panel) are applied to the iris dataset. The code to generate this Figure is available on github.

Although we can see that this is an easy dataset to work with, it allow us to clearly see that the versicolor specie is well separated from the virginica one in the upper panel while there is still some overlap between them in the lower panel. This kind of difference is to be expected since PCA tries to retain most of the variability in the data while LDA tries to retain most of the between-class variance in the data. Note also that in this example the first LD explains more than {99\%} of the between-group variance in the data while the first PC explains {73\%} of the total variability in the data.

Closing remarks

Although I have not applied it on my illustrative example above, pre-processing [3] of the data is important for the application of LDA. Users should transform, center and scale the data prior to the application of LDA. It is also useful to remove near-zero variance predictors (almost constant predictors across units). Given that we need to invert the covariance matrix, it is necessary to have less predictors than samples. Attention is therefore needed when using cross-validation.

References:

[1] Venables, W. N. and Ripley, B. D. (2002). Modern applied statistics with S. Springer.
[2] lda (MASS) help file.
[3] Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling. Springer.

Computing and visualizing PCA in R

Following my introduction to PCA, I will demonstrate how to apply and visualize PCA in R. There are many packages and functions that can apply PCA in R. In this post I will use the function prcomp from the stats package. I will also show how to visualize PCA in R using Base R graphics. However, my favorite visualization function for PCA is ggbiplot, which is implemented by Vince Q. Vu and available on github. Please, let me know if you have better ways to visualize PCA in R.

Computing the Principal Components (PC)

I will use the classical iris dataset for the demonstration. The data contain four continuous variables which corresponds to physical measures of flowers and a categorical variable describing the flowers’ species.

# Load data
data(iris)
head(iris, 3)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa

We will apply PCA to the four continuous variables and use the categorical variable to visualize the PCs later. Notice that in the following code we apply a log transformation to the continuous variables as suggested by [1] and set center and scale. equal to TRUE in the call to prcomp to standardize the variables prior to the application of PCA:

# log transform 
log.ir <- log(iris[, 1:4])
ir.species <- iris[, 5]

# apply PCA - scale. = TRUE is highly 
# advisable, but default is FALSE. 
ir.pca <- prcomp(log.ir,
                 center = TRUE,
                 scale. = TRUE) 

Since skewness and the magnitude of the variables influence the resulting PCs, it is good practice to apply skewness transformation, center and scale the variables prior to the application of PCA. In the example above, we applied a log transformation to the variables but we could have been more general and applied a Box and Cox transformation [2]. See at the end of this post how to perform all those transformations and then apply PCA with only one call to the preProcess function of the caret package.

Analyzing the results

The prcomp function returns an object of class prcomp, which have some methods available. The print method returns the standard deviation of each of the four PCs, and their rotation (or loadings), which are the coefficients of the linear combinations of the continuous variables.

# print method
print(ir.pca)

Standard deviations:
[1] 1.7124583 0.9523797 0.3647029 0.1656840

Rotation:
                    PC1         PC2        PC3         PC4
Sepal.Length  0.5038236 -0.45499872  0.7088547  0.19147575
Sepal.Width  -0.3023682 -0.88914419 -0.3311628 -0.09125405
Petal.Length  0.5767881 -0.03378802 -0.2192793 -0.78618732
Petal.Width   0.5674952 -0.03545628 -0.5829003  0.58044745

The plot method returns a plot of the variances (y-axis) associated with the PCs (x-axis). The Figure below is useful to decide how many PCs to retain for further analysis. In this simple case with only 4 PCs this is not a hard task and we can see that the first two PCs explain most of the variability in the data.

# plot method
plot(ir.pca, type = "l")

The summary method describe the importance of the PCs. The first row describe again the standard deviation associated with each PC. The second row shows the proportion of the variance in the data explained by each component while the third row describe the cumulative proportion of explained variance. We can see there that the first two PCs accounts for more than {95\%} of the variance of the data.

# summary method
summary(ir.pca)

Importance of components:
                          PC1    PC2     PC3     PC4
Standard deviation     1.7125 0.9524 0.36470 0.16568
Proportion of Variance 0.7331 0.2268 0.03325 0.00686
Cumulative Proportion  0.7331 0.9599 0.99314 1.00000

We can use the predict function if we observe new data and want to predict their PCs values. Just for illustration pretend the last two rows of the iris data has just arrived and we want to see what is their PCs values:

# Predict PCs
predict(ir.pca, 
        newdata=tail(log.ir, 2))

          PC1         PC2        PC3         PC4
149 1.0809930 -1.01155751 -0.7082289 -0.06811063
150 0.9712116 -0.06158655 -0.5008674 -0.12411524

The Figure below is a biplot generated by the function ggbiplot of the ggbiplot package available on github.

The code to generate this Figure is given by

library(devtools)
install_github("ggbiplot", "vqv")

library(ggbiplot)
g <- ggbiplot(ir.pca, obs.scale = 1, var.scale = 1, 
              groups = ir.species, ellipse = TRUE, 
              circle = TRUE)
g <- g + scale_color_discrete(name = '')
g <- g + theme(legend.direction = 'horizontal', 
               legend.position = 'top')
print(g)

It projects the data on the first two PCs. Other PCs can be chosen through the argument choices of the function. It colors each point according to the flowers’ species and draws a Normal contour line with ellipse.prob probability (default to {68\%}) for each group. More info about ggbiplot can be obtained by the usual ?ggbiplot. I think you will agree that the plot produced by ggbiplot is much better than the one produced by biplot(ir.pca) (Figure below).

I also like to plot each variables coefficients inside a unit circle to get insight on a possible interpretation for PCs. Figure 4 was generated by this code available on gist.

PCA on caret package

As I mentioned before, it is possible to first apply a Box-Cox transformation to correct for skewness, center and scale each variable and then apply PCA in one call to the preProcess function of the caret package.

require(caret)
trans = preProcess(iris[,1:4], 
                   method=c("BoxCox", "center", 
                            "scale", "pca"))
PC = predict(trans, iris[,1:4])

By default, the function keeps only the PCs that are necessary to explain at least 95% of the variability in the data, but this can be changed through the argument thresh.

# Retained PCs
head(PC, 3)

        PC1        PC2
1 -2.303540 -0.4748260
2 -2.151310  0.6482903
3 -2.461341  0.3463921

# Loadings
trans$rotation

                    PC1         PC2
Sepal.Length  0.5202351 -0.38632246
Sepal.Width  -0.2720448 -0.92031253
Petal.Length  0.5775402 -0.04885509
Petal.Width   0.5672693 -0.03732262

See Unsupervised data pre-processing for predictive modeling for an introduction of the preProcess function.

References:

[1] Venables, W. N., Brian D. R. Modern applied statistics with S-PLUS. Springer-verlag. (Section 11.1)
[2] Box, G. and Cox, D. (1964). An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological) 211-252

 

Introduction to Principal Component Analysis (PCA)

Principal component analysis (PCA) is a dimensionality reduction technique that is widely used in data analysis. Reducing the dimensionality of a dataset can be useful in different ways. For example, our ability to visualize data is limited to 2 or 3 dimensions. Lower dimension can sometimes significantly reduce the computational time of some numerical algorithms. Besides, many statistical models suffer from high correlation between covariates, and PCA can be used to produce linear combinations of the covariates that are uncorrelated between each other.

More technically …

Assume you have {n} observations of {p} different variables. Define {X} to be a {(n \times p)} matrix where the {i}-th column of {X} contains the observations of the {i}-th variable, {i = 1, ..., p}. Each row {x_i} of {X} can be represented as a point in a {p}-dimensional space. Therefore, {X} contains {n} points in a {p}-dimensional space.

PCA projects {p}-dimensional data into a {q}-dimensional sub-space {(q \leq p)} in a way that minimizes the residual sum of squares (RSS) of the projection. That is, it minimizes the sum of squared distances from the points to their projections. It turns out that this is equivalent to maximizing the covariance matrix (both in trace and determinant) of the projected data ([1], [2]).

Assume {\Sigma} to be the covariance matrix associated with {X}. Since {\Sigma} is a non-negative definite matrix, it has an eigendecomposition

\displaystyle \Sigma = C \Lambda C^{-1},

where {\Lambda = diag(\lambda _1, ..., \lambda _p)} is a diagonal matrix of (non-negative) eigenvalues in decreasing order, and {C} is a matrix where its columns are formed by the eigenvectors of {\Sigma}. We want the first principal component {p_1} to be a linear combination of the columns of {X}, {p_1 = aX}, subject to {||a||_2 = 1}. In addition, we want {p_1} to have the highest possible variance {V(p_1) = a^T \Sigma a}. It turns out that {a} will be given by the column eigenvector corresponding with the largest eigenvalue of {\Sigma} (a simple proof of this can be found in [2]). Taking subsequent eigenvectors gives combinations with as large as possible variance that are uncorrelated with those that have been taken earlier.

If we pick the first {q} principal components, we have projected our {p}-dimensional data into a {q}-dimensional sub-space. We can define {R^2} in this context to be the fraction of the original variance kept by the projected points,

\displaystyle R^2 = \frac{\sum _{i=1}^{q} \lambda _i}{\sum _{j=1}^{p} \lambda_j}

Some general advice

  • PCA is not scale invariant, so it is highly recommended to standardize all the {p} variables before applying PCA.
  • Singular Value Decomposition (SVD) is more numerically stable than eigendecomposition and is usually used in practice.
  • How many principal components to retain will depend on the specific application.
  • Plotting {(1-R^2)} versus the number of components can be useful to visualize the number of principal components that retain most of the variability contained in the original data.
  • Two or three principal components can be used for visualization purposes.

References:

[1] Venables, W. N., Brian D. R. Modern applied statistics with S-PLUS. Springer-verlag. (Section 11.1)
[2] Notes from a class given by Brian Junker and Cosma Shalizi at CMU.