This is an old version, view current version.

## 11.4 Unit Vectors and Rotations

Unit vectors correspond directly to angles and thus to rotations. This is easy to see in two dimensions, where a point on a circle determines a compass direction, or equivalently, an angle $$\theta$$. Given an angle $$\theta$$, a matrix can be defined, the pre-multiplication by which rotates a point by an angle of $$\theta$$. For angle $$\theta$$ (in two dimensions), the $$2 \times 2$$ rotation matrix is defined by $R_{\theta} = \begin{bmatrix} \cos \theta & -\sin \theta \\ \sin \theta & \cos \theta \end{bmatrix}.$ Given a two-dimensional vector $$x$$, $$R_{\theta} \, x$$ is the rotation of $$x$$ (around the origin) by $$\theta$$ degrees.

### Angles from unit vectors

Angles can be calculated from unit vectors. For example, a random variable theta representing an angle in $$(-\pi, \pi)$$ radians can be declared as a two-dimensional unit vector then transformed to an angle.

parameters {
unit_vector xy;
}
transformed parameters {
real<lower = -pi(), upper = pi()> theta = atan2(xy, xy);
}

If the distribution of $$(x, y)$$ is uniform over a circle, then the distribution of $$\arctan \frac{y}{x}$$ is uniform over $$(-\pi, \pi)$$.

It might be tempting to try to just declare theta directly as a parameter with the lower and upper bound constraint as given above. The drawback to this approach is that the values $$-\pi$$ and $$\pi$$ are at $$-\infty$$ and $$\infty$$ on the unconstrained scale, which can produce multimodal posterior distributions when the true distribution on the circle is unimodal.

With a little additional work on the trigonometric front, the same conversion back to angles may be accomplished in more dimensions.