Tests whether a Wishart-distributed covariance matrix (or transformation thereof) is proportional to a given matrix.
mauchly.test(object, ...) ## S3 method for class 'mlm': mauchly.test((object, ...)) ## S3 method for class 'SSD': mauchly.test((object, Sigma = diag(nrow = p), T = Thin.row(proj(M) - proj(X)), M = diag(nrow = p), X = ~0, idata = data.frame(index = seq_len(p)), ...))
- object of class
- matrix to be proportional to.
- transformation matrix. By default computed from
- formula or matrix describing the outer projection (see below).
- formula or matrix describing the inner projection (see below).
- data frame describing intra-block design.
- arguments to be passed to or from other methods.
Mauchly's test test for whether a covariance matrix can be assumed to be proportional to a given matrix.
This is a generic function with methods for classes
The basic method is for objects of class
SSD the method for
mlm objects just extracts the SSD matrix and invokes the corresponding method with the same options and arguments. The
T argument is used to transform the observations prior to testing. This typically involves transformation to intra-block differences, but more complicated within-block designs can be encountered, making more elaborate transformations necessary. A matrix
T can be given directly or specified as the difference between two projections onto the spaces spanned by
X, which in turn can be given as matrices or as model formulas with respect to
idata (the tests will be invariant to parametrization of the quotient space
The common use of this test is in repeated measurements designs, with
X = ~1. This is almost, but not quite the same as testing for compound symmetry in the untransformed covariance matrix.
Notice that the defaults involve
p, which is calculated internally as the dimension of the SSD matrix, and a couple of hidden functions in the stats namespace, namely
proj which calculates projection matrices from design matrices or model formulas and
Thin.row which removes linearly dependent rows from a matrix until it has full row rank.
An object of class
T. W. Anderson (1958). An Introduction to Multivariate Statistical Analysis. Wiley.
The p-value differs slightly from that of SAS because a second order term is included in the asymptotic approximation in R.
utils::example(SSD) # Brings in the mlmfit and reacttime objects ### traditional test of intrasubj. contrasts mauchly.test(mlmfit, X = ~1) ### tests using intra-subject 3x2 design idata <- data.frame(deg = gl(3, 1, 6, labels = c(0,4,8)), noise = gl(2, 3, 6, labels = c("A","P"))) mauchly.test(mlmfit, X = ~ deg + noise, idata = idata) mauchly.test(mlmfit, M = ~ deg + noise, X = ~ noise, idata = idata)
Documentation reproduced from R 2.15.3. License: GPL-2.