Description Usage Arguments Value Author(s) References Examples
summary
method for class mvlm
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object 
Output from 
... 
Further arguments passed to or from other methods. 
Calling
summary(mvlm.res)
produces a data frame comprised of:
Statistic 
Value of the corresponding test statistic. 
Numer DF 
Numerator degrees of freedom for each test statistic. 
Pseudo R2 
Size of the corresponding (omnibus or conditional) effect on the multivariate outcome. Note that the intercept term does not have an estimated effect size. 
pvalue 
The pvalue for each (omnibus or conditional) effect. 
In addition to the information in the three columns comprising
summary(mvlm.res)
, the mvlm.res
object also contains:
p.prec 
A data.frame reporting the precision of each pvalue.
These are the maximum error bound of the pvalues reported by the

y.rsq 
A matrix containing in its first row the overall variance explained by the model for variable comprising Y (columns). The remaining rows list the variance of each outcome that is explained by the conditional effect of each predictor. 
beta.hat 
Estimated regression coefficients. 
adj.n 
Adjusted sample size used to determine whether or not the asmptotic properties of the model are likely to hold. See McArtor et al. (under review) for more detail. 
data 
Original input data and the 
Note that the printed output of summary(res)
will truncate pvalues
to the smallest trustworthy values, but the object returned by
summary(mvlm.res)
will contain the pvalues as computed. If the error
bound of the Davies algorithm is larger than the pvalue, the only conclusion
that can be drawn with certainty is that the pvalue is smaller than (or
equal to) the error bound.
Daniel B. McArtor ([email protected]) [aut, cre]
Davies, R. B. (1980). The Distribution of a Linear Combination of chisquare Random Variables. Journal of the Royal Statistical Society. Series C (Applied Statistics), 29(3), 323333.
Duchesne, P., & De Micheaux, P.L. (2010). Computing the distribution of quadratic forms: Further comparisons between the LiuTangZhang approximation and exact methods. Computational Statistics and Data Analysis, 54(4), 858862.
McArtor, D. B., Grasman, R. P. P. P., Lubke, G. H., & Bergeman, C. S. (under review). A new approach to conducting linear model hypothesis tests with a multivariate outcome.
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