mauchly.test {stats} | R Documentation |

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` |
object of class |

`Sigma` |
matrix to be proportional to. |

`T` |
transformation matrix. By default computed from |

`M` |
formula or matrix describing the outer projection (see below). |

`X` |
formula or matrix describing the inner projection (see below). |

`idata` |
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 `"mlm"`

and
`"SSD"`

.

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
`M`

and `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 `M/X`

).

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 `"htest"`

The p-value differs slightly from that of SAS because a second order term
is included in the asymptotic approximation in **R**.

T. W. Anderson (1958). *An Introduction to Multivariate
Statistical Analysis.* Wiley.

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)

[Package *stats* version 3.4.0 Index]