summary.mixed.mdmr {MDMR}

Summarizing Mixed MDMR Results

Description

summary method for class mixed.mdmr

Usage

## S3 method for class 'mixed.mdmr'
summary(object, ...)

Arguments

object

Output from mixed.mdmr

...

Further arguments passed to or from other methods.

Value

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 davies function in CompQuadForm. If permutation p-values were computed, it is the standard error of each permutation p-value.

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.

Author(s)

Daniel B. McArtor ([e-mail]) [aut, cre]

References

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).

Examples

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)