Description Usage Arguments Value Author(s) References Examples

`summary`

method for class `mixed.mdmr`

1 2 |

`object` |
Output from |

`...` |
Further arguments passed to or from other methods. |

Calling
`summary(mdmr.res)`

produces a data frame comprised of:

`Statistic` |
Value of the corresponding MDMR test statistic |

`p-value` |
The p-value for each effect. |

In addition to the information in the three columns comprising
`summary(res)`

, the `res`

object also contains:

`p.prec` |
A data.frame reporting the precision of each p-value. If
analytic p-values were computed, these are the maximum error bound of the
p-values reported by the |

Note that the printed output of `summary(res)`

will truncate p-values
to the smallest trustworthy values, but the object returned by
`summary(res)`

will contain the p-values as computed. The reason for
this truncation differs for analytic and permutation p-values. For an
analytic p-value, if the error bound of the Davies algorithm is larger than
the p-value, the only conclusion that can be drawn with certainty is that
the p-value is smaller than (or equal to) the error bound.

Daniel B. McArtor (dmcartor@gmail.com) [aut, cre]

Davies, R. B. (1980). The Distribution of a Linear Combination of chi-square Random Variables. Journal of the Royal Statistical Society. Series C (Applied Statistics), 29(3), 323-333.

Duchesne, P., & De Micheaux, P. L. (2010). Computing the distribution of quadratic forms: Further comparisons between the Liu-Tang-Zhang approximation and exact methods. Computational Statistics and Data Analysis, 54(4), 858-862.

McArtor, D. B. (2017). Extending a distance-based approach to multivariate multiple regression (Doctoral Dissertation).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
data("clustmdmrdata")
# Get distance matrix
D <- dist(Y.clust)
# Regular MDMR without the grouping variable
mdmr.res <- mdmr(X = X.clust[,1:2], D = D, perm.p = FALSE)
# Results look significant
summary(mdmr.res)
# Account for grouping variable
mixed.res <- mixed.mdmr(~ x1 + x2 + (x1 + x2 | grp),
data = X.clust, D = D)
# Signifance was due to the grouping variable
summary(mixed.res)
``` |

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