knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

```
library(MixMatrix)
```

In the `MixMatrix`

package, there are two functions for training a linear or quadratic classifier.
The usage is fairly similar to the function `MASS::lda()`

or `MASS::qda()`

, however it requires the input
as an array and the group variable provided as a vector (that is, it cannot handle
data frames or the formula interface directly, which is reasonable, as there is no immediately
clear way to make that work for a collection of matrices).

set.seed(20180222) library('MixMatrix') A <- rmatrixnorm(30, mean = matrix(0, nrow=2, ncol=3)) # creating the three groups B <- rmatrixnorm(30, mean = matrix(c(1, 0), nrow = 2, ncol = 3)) C <- rmatrixnorm(30, mean = matrix(c(0, 1), nrow = 2, ncol = 3)) ABC <- array(c(A,B,C), dim = c(2,3,90)) # combining into on array groups <- factor(c(rep("A", 30), rep("B", 30), rep("C", 30))) # labels prior = c(30, 30, 30) / 90 # equal prior matlda <- matrixlda(x = ABC, grouping = groups, prior = prior) # perform LDA matqda <- matrixqda(x = ABC, grouping = groups, prior = prior) # perform QDA

This does not sphere the data or extract an SVD or Fisher discriminant scores - it is a simple linear/quadratic discriminant function based on the likelihood function.

The `matrixlda`

function presumes equal covariance matrices among the groups while `matrixqda`

fits separate covariance for each groups.

It is possible to set variance or mean restrictions from the `MLmatrixnorm`

and `MLmatrixt`

functions using the `...`

argument. These restrictions are applied to all groups.

The `predict`

method for these objects works in much the same way as for `lda`

or `qda`

objects: provide the function and the new data, then it will return
the MAP class assignments and the posterior. If no new data is
provided, it will attempt to run it on the original data if it is available
in the environment.

ABC[, , c(1, 31, 61)] # true class memberships: A, B, C #predict the membership of the first observation of each group predict(matlda, ABC[, , c(1, 31, 61)]) #predict the membership of the first observation of each group predict(matqda, ABC[, , c(1, 31, 61)])

In this example, points from classes A, B, and C were selected and they ended up being classified as B, B, and A. The QDA and LDA rules had only minor disagreements, which is to be expected when they do truly have the same covariances.

Suppose there are two populations, $\pi_1$ and $\pi_2$ with prior probabilities
of belonging to these classes, $p_1$ and $p_2$. Define a function, $c(1|2)$ as
the cost of misclassifying a member of population $\pi_2$ as a member of class
$1$ (and vice versa). Further, define $P(1|2)$ as the probability of classifying
a member of population $\pi_2$ as a member of class $1$ (and vice versa). Then
we define the *expected cost of misclassification* as:

[ECM = c(2|1)P(2|1)p_1 + c(1|2)P(1|2)p_2 ]

A reasonable classification rule based on ECM is the following:

Classify as class $1$ if:

[ \frac{f_1(x)}{f_2(x)} \geq \frac{c(1|2) p_2}{c(2|1)p_1} ]

Where $f_i(x)$ is the probability density function for group $\pi_i$ evaluated at $x$.

Recall the probability density function for a matrix variate normal distribution:

[f(\mathbf{X};\mathbf{M}, \mathbf{U}, \mathbf{V}) = \frac{\exp\left( -\frac{1}{2} \, \mathrm{tr}\left[ \mathbf{V}^{-1} (\mathbf{X} - \mathbf{M})^{T} \mathbf{U}^{-1} (\mathbf{X} - \mathbf{M}) \right] \right)}{(2\pi)^{np/2} |\mathbf{V}|^{n/2} |\mathbf{U}|^{p/2}} ]

$\mathbf{X}$ and $\mathbf{M}$ are $n \times p$, $\mathbf{U}$ is $n \times n$ and describes the covariance relationship between the rows, and $\mathbf{V}$ is $p \times p$ and describes the covariance relationship between the columns.

A decision rule for this case:

\begin{eqnarray}
R(\mathbf{X}) & = & \mathrm{trace}\big[ -\frac{1}{2}(\mathbf{V}_1^{-1} \mathbf{X}^{T} \mathbf{U}_1^{-1} \mathbf{X} - \mathbf{V}_2^{-1} \mathbf{X}^{T} \mathbf{U}_2^{-1} \mathbf{X}) \
& & +(\mathbf{V}_1^{-1} \mathbf{M}_1^{T} \mathbf{U}_1^{-1} - \mathbf{V}_2^{-1} \mathbf{M}_2^{T} \mathbf{U}_2^{-1}) \mathbf{X} \
& & -\frac{1}{2}(\mathbf{V}_1^{-1} \mathbf{M}_1^{T} \mathbf{U}_1^{-1} \mathbf{M}_1 - \mathbf{V}_2^{-1} \mathbf{M}_2^{T} \mathbf{U}_2^{-1} \mathbf{M}_2) \big] \
& & -\frac{1}{2}(p (\log|\mathbf{U}_1|-\log|\mathbf{U}_2|)+ n(\log|\mathbf{V}_1|-\log|\mathbf{V}_2|) )

\end{eqnarray}

If $R(\mathbf{X}) \geq \log(c(1|2)p_2) - \log(c(2|1)p_1)$, assign $\mathbf{X}$ to group $1$, otherwise assign to group $2$.

In the multivariate case, this is equivalent to the LDA/QDA rules - term (1) is the quadratic form which vanishes in case of equal covariances between groups, term (2) is the LDA term, and terms (3) and (4) are constants which depend on the parameters and not $\mathrm{X}$.

Typically, the models we have used have implicitly used an equal probability prior and an equal cost of misclassification, but other inputs can be specified. In case of equal priors and equal cost of misclassification, this term is 0.

If the two groups have the same covariances, then this simplifies. The classification rule is then: \begin{eqnarray} R(\mathbf{X}) & = & \mathrm{trace}\big[ (\mathbf{V}^{-1} (\mathbf{M}_1 -\mathbf{M}_2)^{T}\mathbf{U}^{-1}) \mathbf{X} \ & & -\frac{1}{2}(\mathbf{V}^{-1} \mathbf{M}_1^{T} \mathbf{U}^{-1} \mathbf{M}_1 - \mathbf{V}^{-1} \mathbf{M}_2^{T} \mathbf{U}^{-1} \mathbf{M}_2) \big] \ & \geq & \log(c(1|2)p_2) - \log(c(2|1)p_1) \end{eqnarray}

Classify to group $1$ if the last term is true. Note this is linear in $\mathbf{X}$. This is directly analogous to LDA in the multivariate case.

The factor $R$ for each group $g$ in a QDA setting is:

\begin{eqnarray} R_g(\mathbf{X}) & = & \mathrm{trace}\big[ -\frac{1}{2}(\mathbf{V}_g^{-1} \mathbf{X}^{T} \mathbf{U}_g^{-1} \mathbf{X} +(\mathbf{V}_g^{-1} \mathbf{M}_g^{T} \mathbf{U}_g^{-1}) \mathbf{X} -\frac{1}{2}(\mathbf{V}_g^{-1} \mathbf{M}_g^{T} \mathbf{U}_g^{-1} \mathbf{M}_g) \big] \ & & -\frac{1}{2}(p (\log|\mathbf{U}_g|)+ n(\log|\mathbf{V}_g|) ) + \log p_g \end{eqnarray} When $U_i = U_j$ for all groups $i,j$, several of these terms cancel. The posterior probability is:

[ P(\mathbf{X} \in g) = \frac{ \exp (R_g (\mathbf{X}))}{\sum_i \exp (R_i(\mathbf{X}))} ] with the bottom sum being over all groups $i$.

The objects returned by `matrixlda`

and `matrixqda`

are S3 classes. See below example output:

matlda matqda

This vignette was built using `rmarkdown`

.

```
sessionInfo()
```

labs = knitr::all_labels() labs = labs[!labs %in% c("setup", "toc", "getlabels", "allcode")]

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.