Description Usage Arguments Details Value Author(s) References Examples
mvlm
is used to fit linear models with a multivariate outcome. It uses
the asymptotic null distribution of the multivariate linear model test
statistic to compute pvalues (McArtor et al., under review). It therefore
alleviates the need to use approximate pvalues based Wilks' Lambda, Pillai's
Trace, the HotellingLawley Trace, and Roy's Greatest Root.
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formula 
An object of class 
data 
Mandatory 
n.cores 
Number of cores to use in parallelization through the

start.acc 
Starting accuracy of the Davies (1980) algorithm
implemented in the 
contr.factor 
The type of contrasts used to test unordered categorical
variables that have type 
contr.ordered 
The type of contrasts used to test ordered categorical
variables that have type 
Importantly, the outcome of formula
must be a matrix
, and the
object passed to data
must be a data frame containing all of the
variables that are named as predictors in formula
.
The conditional effects of variables of type factor
or ordered
in data
are computed based on the type of contrasts specified by
contr.factor
and contr.ordered
. If data
contains an
(ordered or unordered) factor with k
levels, a k1
degree of
freedom test will be conducted corresponding to that factor and the specified
contrast structure. If, instead, the user wants to assess k1
separate
single DF tests that comprise this omnibus effect (similar to the approach
taken by lm
), then the appropriate model matrix should be formed in
advance and passed to mvlm
directly in the data
parameter. See
the package vigentte for an example by calling
vignette('mvlmvignette')
.
An object with nine elements and a summary function. 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 
formula 
The formula passed to 
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 (dmcartor@nd.edu) [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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