summary method for class
Further arguments passed to or from other methods.
summary(mdmr.res) produces a data frame comprised of:
Value of the corresponding MDMR test statistic
The p-value for each effect.
In addition to the information in the three columns comprising
res object also contains:
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 (firstname.lastname@example.org) [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).
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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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