We will develop a linear discriminant analysis example. The exercise was originally published in

**.**

*"An Introduction to Statistical Learning. With applications in R"*by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Springer 2015The example we will develop is about classifying when the market value will rise (UP) or fall (Down).

We will carry out the exercise verbatim as published in the aforementioned reference and only with slight changes in the coding style.

For more details on the models, algorithms and parameters interpretation, it is recommended to check the aforementioned reference or any other bibliography of your choice.

**### "An Introduction to Statistical Learning.**

### with applications in R" by Gareth James,

### Daniela Witten, Trevor Hastie and Robert Tibshirani.

### Springer 2015.

### install and load required packages

### with applications in R" by Gareth James,

### Daniela Witten, Trevor Hastie and Robert Tibshirani.

### Springer 2015.

### install and load required packages

library(ISLR)

library(psych)

library(MASS)

**### perform linear discriminant analysis LDA on the stock**

### market data

### market data

train <- (Smarket$Year < 2005)

Smarket.2005 <- Smarket[!train, ]

Direction.2005 <- Smarket$Direction[!train]

lda.fit <- lda(Direction ~ Lag1 + Lag2 , data = Smarket , subset = train )

lda.fit

**### LDA indicates that 49.2% of training observations**

### correspond to days during wich the market went down

### group means suggest that there is a tendency for the

### previous 2 days returns to be negative on days when the

### market increases, and a tendency for the previous days

### returns to be positive on days when the market declines

### coefficients of linear discriminants output provides the

### linear combination of Lag1 and Lag2 that are used to form

### the LDA decision rule

### if (−0.642 * Lag1 − 0.514 * Lag2) is large, then the LDA

### classifier will predict a market increase, and if it is

### small, then the LDA classifier will predict a market decline

### plot() function produces plots of the linear discriminants,

### obtained by computing (−0.642 * Lag1 − 0.514 * Lag2) for

### each of the training observations

### correspond to days during wich the market went down

### group means suggest that there is a tendency for the

### previous 2 days returns to be negative on days when the

### market increases, and a tendency for the previous days

### returns to be positive on days when the market declines

### coefficients of linear discriminants output provides the

### linear combination of Lag1 and Lag2 that are used to form

### the LDA decision rule

### if (−0.642 * Lag1 − 0.514 * Lag2) is large, then the LDA

### classifier will predict a market increase, and if it is

### small, then the LDA classifier will predict a market decline

### plot() function produces plots of the linear discriminants,

### obtained by computing (−0.642 * Lag1 − 0.514 * Lag2) for

### each of the training observations

plot (lda.fit)

**### predictions**

**### the LDA and logistic regression predictions are almost identical**lda.pred <- predict(lda.fit, Smarket.2005)

names(lda.pred)

lda.class <- lda.pred$class

table(lda.class, Direction.2005)

mean(lda.class == Direction.2005)

**### apply a 50% threshold to the posterior probabilities allows**

### us to recreate the predictions in lda.pred$class

### us to recreate the predictions in lda.pred$class

sum(lda.pred$posterior [ ,1] >= 0.5)

sum(lda.pred$posterior [ ,1] < 0.5)

**### posterior probability output by the model corresponds to**

### the probability that the market will decrease

### the probability that the market will decrease

lda.pred$posterior[1:20 ,1]

lda.class[1:20]

**### use a posterior probability threshold other than 50 % in order**

### to make predictions

### suppose that we wish to predict a market decrease only if we

### are very certain that the market will indeed decrease on that

### day-say, if the posterior probability is at least 90%

### to make predictions

### suppose that we wish to predict a market decrease only if we

### are very certain that the market will indeed decrease on that

### day-say, if the posterior probability is at least 90%

sum(lda.pred$posterior[ ,1] > 0.9)

**### No days in 2005 meet that threshold! In fact, the greatest**

### posterior probability of decrease in all of 2005 was 52.02%

### posterior probability of decrease in all of 2005 was 52.02%

You can get the example in:

https://github.com/pakinja/Data-R-Value