formula or terms describing the model. The response
needs to be a matrix.
object
an object that inherits from class
manova.
OPTIONAL ARGUMENTS:
data
a
data.frame in which to interpret the variables named in the
formula, or in the
subset and the
weights argument.
If this is missing, then the variables in the formula should be on the
search list.
weights
vector of observation weights; if supplied,
the algorithm fits to minimize the sum
of the weights multiplied into the squared residuals.
The length of
weights must be the same as the number of observations.
The weights must be nonnegative and it is strongly recommended that they
be strictly positive, since zero weights are ambiguous, compared to use
of the
subset argument.
subset
expression saying which subset of the rows of the data
should be used in the fit.
This can be a logical vector (which is replicated to have length equal
to the number of observations), or a numeric vector indicating which
observation numbers are to be included, or a character vector of the
row names to be included.
All observations are included by default.
contrasts
a list of contrasts to be used for some or all of
the factors appearing as variables in the model formula.
The names of the list should be the names of the
corresponding variables, and the elements should either be
contrast-type matrices (matrices with as many rows as
levels of the factor and with columns linearly independent of
each other and of a column of one's), or else they should
be functions that compute such contrast matrices.
na.omit.p
if
TRUE, then any observation with missing values are removed
from the analysis.
If
FALSE and there are missing values then the function will exit
with a message that missing values are not allowed.
If
na.omit.p is
TRUE then
na.action is set to
na.omit in the
call to
lm.
If
na.omit.p is
FALSE then
na.action is set to
na.fail in the
call to
lm.
print.object.p
if
TRUE, a short analysis of variance table is printed.
This output is from the function
print.aov.
anova.p
if
TRUE, an ANOVA table is printed.
This output is from the function
summary.manova.
test
character string partially matching one of: "pillai",
"wilks lambda", "hotelling-lawley", or "roy largest" (a
partial match is sufficient).
coef.p
if
TRUE, coefficients of the least squares fit of the
response(s) on the model matrix are printed. The column names of the
matrix of coefficients are the names of the single-
degree-of-freedom effects (the linearly independent
columns of the model matrix).
This output is from the function
coef.lm.
dummy.coef.p
if
TRUE, coefficients in terms of the original dummy variable
coding of factors are printed.
This output is from the function
dummy.coef.
save.results
a character string for the name of the data frame to save the
fit and residuals in.
If data frame with this name already exists in database 1 and it has the
appropriate number of rows then the saved values will be appended
to the data frame.
If the object already exist in database 1 and it is not a data frame
or it does not have the appropriate number of rows then a new name
is created by appending a number to
save.results and the results are
saved in the data frame with the new name.
show.p
if
TRUE, the
save.results data will be displayed in a Data window.
save.fitted.p
if
TRUE, the fitted values from the regression are saved in the
data frame
save.results.
save.resid.p
if
TRUE, the residuals from the regression are saved in the
data frame
save.results.
VALUE:
an object of class
"manova" which inherits from classes
"maov"
,
"mlm",
"aov" and
"lm".
See the
aov.object and
manova help files for details
on the components of this object.
A
manova object is essentially the same as an
aov object
from a multiresponse model, only the class is different.
The most important difference is that there is a method
for summary specific to
manova objects.