Description Usage Arguments Details Value Warning Note Author(s) See Also Examples
Methods for automatic linear models for data frames of class design
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  lm(formula, ...)
## Default S3 method:
lm(formula, data, subset, weights, na.action, method = "qr",
model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
contrasts = NULL, offset, ...)
## S3 method for class 'design'
lm(formula, ..., response=NULL, degree=NULL, FUN=mean,
use.center=NULL, use.star=NULL, use.dummies=FALSE)
aov(formula, ...)
## Default S3 method:
aov(formula, data = NULL, projections = FALSE, qr = TRUE,
contrasts = NULL, ...)
## S3 method for class 'design'
aov(formula, ..., response=NULL, degree=NULL, FUN=mean,
use.center=FALSE)
## S3 method for class 'lm.design'
print(x, ...)
## S3 method for class 'lm.design'
summary(object, ...)
## S3 method for class 'lm.design'
coef(object, ...)
## S3 method for class 'summary.lm.design'
print(x, ...)
## S3 method for class 'aov.design'
print(x, ...)
## S3 method for class 'aov.design'
summary(object, ...)
## S3 method for class 'summary.aov.design'
print(x, ...)
lm.design
summary.lm.design
aov.design
summary.aov.design

formula 
for the default method, cf. documentation for 
... 
further arguments to functions 
response 
character string giving the name of the response variable
(must be among the responses of For the default 
degree 
degree for the formula; if 
FUN 
function for the 
use.center 

use.star 

use.dummies 
logical indicating whether the error dummies of a Plackett Burman design are to be used in the formula (ignored for all other types of designs). 
projections 
logical indicating whether the projections should be returned; for orthogonal arrays, these are helpful, as they provide the estimated deviation from the overall average attributed to each particular factor; it is not recommended to use them with unbalanced designs 
x 
object of class 
object 
object of class 
lm.design 
a class that is identical in content to class 
summary.lm.design 
a class that is identical in content to class 
data 
like in 
subset 
like in 
weights 
like in 
na.action 
like in 
method 
like in 
model 
like in 
y 
like in 
qr 
like in 
singular.ok 
like in 
contrasts 
like in 
offset 
like in 
The aov
and lm
methods for class design
conduct a default linear model analysis for data frames of
class design
that do contain at least one response.
The intention for providing default analyses is to support convenient quick inspections. In many cases, there will be good reasons to customize the analysis, for example by including some but not all effects of a certain degree. Also, it may be statistically more wise to work with mixed models for some types of design. The default analyses must not be taken as a statistical recommendation!
The choice of default analyses
has been governed by simplicity: It uses fixed effects only and does either
main effects models (degree=1
, default for pb
and oa
designs),
models with main effects and 2factor interactions (degree=2
,
default for most designs) or second order models (that contain
quadratic effects in addition to the 2factor interactions, unchangeable default
for designs with quantitative variables). The degree
parameter can be used
to modify the degree of interactions. If blocks are
present, the block main effect is always entered as a fixed effect without interactions.
Designs with center points are per default analysed without the center points; the main
reason for this is convenient usage of functions DanielPlot
,
MEPlot
and IAPlot
from package FrF2.
With the use.center
option, this default can be changed; in this case, significance
of the center point indicator implies that there are one or more quadratic effect(s)
in the model.
Designs with repeated measurements (repeat.only=TRUE
) and parameter
designs of long format are treated by aggregate.design
with aggregation function FUN
(default: means are calculated)
before applying a linear model.
For designs with repeated measurements (repeat.only=TRUE
) and parameter
designs of wide format, the default is to use the first aggregated response,
if the design has been aggregated already. For a so far unaggregated design,
the default is to treat the design by aggregate.design
,
using the function FUN
(default: mean) and then use the first response.
The defaults can be overridden by specifying response
: Here,
response
can not only be one of the current responses but also a column name
of the responselist
element of the design.info
attribute of the
design (i.e. a response name from the long version of the design).
The implementation of the formulae is not done in functions lm.design
or
aov.design
themselves
but based on the method for function formula (formula.design
).
The print
methods prepend the formula and the number of experimental runs
underlying the analysis to the default printout.
The purpose of this is meaningful output in case a call from
inside function lm.design
or aov.design
(methods for functions lm
and aov
)
does not reveal enough information, and another pointer that center points have been
omitted or repeated measurements aggregated over. The coef
method for objects
of class lm.design
suppresses NA
coefficients, i.e.
returns valid coefficients only. For aov
objects, this is the default
anyway.
The value for the lm
functions is a linear model object,
exactly like for function lm
,
except for the added class lm.design
in case of the method for class design
,
and an added list element WholePlotEffects
for split plot designs.
The value for the aov
functions is an aov object,
exactly like for function aov
,
and an added list element WholePlotEffects
for split plot designs.
The value of the summary
functions for class lm.design
and
aov.design
respectively
is a linear model or aov summary, exactly like documented in summary.lm
or summary.aov
,
except for the added classes summary.lm.design
or summary.aov.design
,
and an added list element WholePlotEffects
(for summary.lm.design
)
or attribute (for summary.aov.design
) for split plot designs.
The print functions return NULL
; they are used for their side effects only.
The generics for lm
and aov
replace the functions
from package stats. For normal use, this is not an issue, because their
default methods are exactly the functions from package stats.
However, when programming on the language (or when using a package that relies on
such constructs), you may see unexpected results.
For example, match.call(lm)
returns a different result, depending on
whether or not package DoE.base is loaded. This can be avoided by
explicitly requesting e.g. match.call(stats::lm)
, which always works
in the same way.
Please report any additional issues that you may experience.
The package is currently subject to intensive development; most key functionality is now included. Some changes to input and output structures may still occur.
Ulrike Groemping
See also the information on class design
and its formula method formula.design
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36  oa12 < oa.design(nlevels=c(2,2,6))
## add a few variables to oa12
responses < cbind(y=rexp(12),z=runif(12))
oa12 < add.response(oa12, responses)
## want treatment contrasts rather than the default
## polynomial contrasts for the factors
oa12 < change.contr(oa12, "contr.treatment")
linmod.y < lm(oa12)
linmod.z < lm(oa12, response="z")
linmod.y
linmod.z
summary(linmod.y)
summary(linmod.z)
## examples with aggregation
plan < oa.design(nlevels=c(2,6,2), replications=2, repeat.only=TRUE)
y < rnorm(24)
z < rexp(24)
plan < add.response(plan, cbind(y=y,z=z))
lm(plan)
lm(plan, response="z")
lm(plan, FUN=sd)
## wide format
plan < reptowide(plan)
plan
design.info(plan)$responselist
## default: aggregate variables for first column of responselist
lm(plan)
## request z variables instead (z is the column name of response list)
lm(plan, response="z")
## force analysis of first z measurement only
lm(plan, response="z.1")
## use almost all options
## (option use.center can only be used with center point designs
## from package FrF2)
summary(lm(plan, response="z", degree=2, FUN=sd))

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