  Efficient methods for computing with matrices over finite fields often
involve randomised algorithms, where matrices with a certain property are
sought via repeated random selection. Complexity analyses for these algorithms
require knowledge of the proportion of relevant matrices in the ambient group
or algebra. We introduce a method for estimating proportions of families $N$ of
elements in the algebra of all $d\times d$ matrices over a field of order $q$,
where membership of a matrix in $N$ depends only on its `invertible part'. The
method is based on estimating proportions of certain subsets of
$\text{GL}(d,q)$ depending on $N$, so that existing estimation techniques for
nonsingular matrices can be leveraged to deal with families containing singular
matrices. As an application we investigate primary cyclic matrices, which are
used in the Holt-Rees MEAT-AXE algorithm for testing irreducibility of matrix
algebras.
