Description Usage Arguments Details
View source: R/multipleanovas.R
Computes multiple ANOVAs.
1 2 3 4 5 
dependents 
The outcome variables. 
independent 
The factor representing the groups. 
subset 
An optional vector specifying a subset of observations to be
used in the fitting process, or, the name of a variable in 
weights 
An optional vector of sampling weights, or, the name or, the
name of a variable in 
compare 
One of 
correction 
The multiple comparison adjustment method: 
robust.se 
Computes standard errors that are robust to violations of the assumption of constant variance. This parameter is ignored if weights are applied (as weights already employ a sandwich estimator). 
alternative 
The alternative hypothesis: "Two sided", "Greater", or "Less". The main application of this is when Compare us set 'To first' (e.g., if testing a new product, where the purpose is to work out of the new product is superior to an existing product, "Greater" would be chosen). 
missing 
How missing data is to be treated in the ANOVA. Options:

show.labels 
Shows the variable labels, as opposed to the labels, in the outputs, where a variables label is an attribute (e.g., attr(foo, "label")). 
seed 
The random number seed used when evaluating the multivariate tdistribution. 
p.cutoff 
The alpha level to be used in testing. 
data 
An optional 
... 
Other parameters to be passed to 
Conducts multiple OneWayANOVA
s, and puts them in a list. If correction
is
"Table FDR"
, the false discovery rate correction is applied across the entire table. All
other corrections are performed within rows. Additional detail about the other parameters can be found in OneWayANOVA
.
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