summary method for class mdmr
# S3 method for mdmr
summary(object, ...)Calling
summary(mdmr.res) produces a data frame comprised of:
Value of the corresponding MDMR test statistic
Size of the corresponding effect on the distance matrix
The p-value for each effect.
In addition to the information in the three columns comprising
summary(res), the 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 davies function in CompQuadForm. If
permutation p-values were computed, it is the standard error of each
permutation p-value.
A vector of the eigenvalues of G (if
return.lambda = T).
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.
Output from mdmr
Further arguments passed to or from other methods.
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., 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.
# --- 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)
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