dist

Compute the variance, covariance or covariance-matrix.

---

Usage 1

dist(xscalar, yscalar)

dist(xvector, yvector)

Result 1

The euclidan distance d_scalar of x and y. The two arguments must be scalars or vectors with the same length.

---

Usage 2

dist(xvector)

dist(xmatrix)

Result 2

The matrix d with the euclidian distances of all elements or row vectors of x. The result is a matrix with nrow(x) rows and columns.

xvector	-> d[i,j] = dist(x[i], x[j])
xmatrix	-> d[i,j] = dist(x[i,*], x[j,*])
	with: i,j = 0 .. ncol(x)

---

Usage 3

dist(xvector, yscalar)

dist(xmatrix, yvector)

Result 3

The vector d with the euclidian distances of all elements or row vectors of x to y. The result is a vector with nrow(x) rows and columns.

xvector,yscalar	-> d[i] = dist(x[i], y)
xmatrix,yvector	-> d[i,j] = dist(x[i,*], y) (ncol(x) must be equal to nrow(y)
	with: i = 0 .. ncol(x)

---

Usage 4

dist(xmatrix, flag)

Result 4

The matrix d with the euclidian distances of the row vectors (flag=0) or column vectors (flag=1) or x.

flag=0	-> d[i,j] = dist(x[i,*], x[j,*])	, with: i,j = 0 .. nrow(x)
flag=1	-> d[i,j] = dist(x[*,i], x[*,j])	, with: i,j = 0 .. ncol(x)

See also

var, corr

<function list>