# summary.mdmr: Summarizing MDMR Results In dmcartor/mdmr: Multivariate Distance Matrix Regression

## Description

`summary` method for class `mdmr`

## Usage

 ```1 2``` ```## S3 method for class 'mdmr' summary(object, ...) ```

## Arguments

 `object` Output from `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 `Pseudo R2` Size of the corresponding effect on the distance matrix `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. `lambda` A vector of the eigenvalues of `G` (if `return.lambda = T`). `nperm` Number of permutations used. Will read `NA` if analytic p-values were computed

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. For a permutation test, the estimated p-value will be zero if no permuted test statistics are greater than the observed statistic, but the zero p-value is only a product of the finite number of permutations conduted. The only conclusion that can be drawn is that the p-value is smaller than `1/nperm`.

## Author(s)

Daniel B. McArtor (dmcartor@gmail.com) [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., Lubke, G. H., & Bergeman, C. S. (2017). Extending multivariate distance matrix regression with an effect size measure and the distribution of the test statistic. Psychometrika, 82, 1052-1077.

## Examples

 ```1 2 3 4 5 6``` ```# --- The following two approaches yield equivalent results --- # # Approach 1 data(mdmrdata) D <- dist(Y.mdmr, method = "euclidean") mdmr.res <- mdmr(X = X.mdmr, D = D) summary(mdmr.res) ```

dmcartor/mdmr documentation built on May 15, 2019, 9:19 a.m.