# makeKernel: Compute kernel matrix In classmap: Visualizing Classification Results

 makeKernel R Documentation

## Compute kernel matrix

### Description

Computes kernel value or kernel matrix, where the kernel type is extracted from an svm trained by `e1071::svm`.

### Usage

``````makeKernel(X1, X2 = NULL, svfit)
``````

### Arguments

 `X1` first matrix (or vector) of coordinates. `X2` if not `NULL`, second data matrix or vector. If NULL, `X2` is assumed equal to `X1`. `svfit` output from `e1071::svm`

.

### Value

the kernel matrix, of dimensions `nrow(X1)` by `nrow(X2)`. When both `X1` and `X2` are vectors, the result is a single number.

### Author(s)

Raymaekers J., Rousseeuw P.J.

### References

Raymaekers J., Rousseeuw P.J., Hubert M. (2021). Class maps for visualizing classification results. Technometrics, appeared online. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00401706.2021.1927849")}(link to open access pdf)

`makeFV`

### Examples

``````
library(e1071)
set.seed(1); X <- matrix(rnorm(200 * 2), ncol = 2)
X[1:100, ] <- X[1:100, ] + 2
X[101:150, ] <- X[101:150, ] - 2
y <- as.factor(c(rep("blue", 150), rep("red", 50))) # two classes
# We now fit an SVM with radial basis kernel to the data:
set.seed(1) # to make the result of svm() reproducible.
svmfit <- svm(y~.,  data = data.frame(X = X, y = y),  scale = FALSE,
kernel = "radial", cost = 10, gamma = 1, probability = TRUE)
Kxx <- makeKernel(X, svfit = svmfit)
# The result is a square kernel matrix:
dim(Kxx) # 200 200
Kxx[1:5, 1:5]

# For more examples, we refer to the vignette:
## Not run:
vignette("Support_vector_machine_examples")

## End(Not run)
``````

classmap documentation built on April 23, 2023, 5:09 p.m.