DoE.base (version 0.11-1)

lm method for class design objects: lm method for class design objects

Description

Method for automatic linear models for data frames of class design

Usage

lm(formula, ...)
## S3 method for class 'default':
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=FALSE)
## S3 method for class 'lm.design':
print(x, ...)
## S3 method for class 'lm.design':
summary(object, ...)
## S3 method for class 'summary.lm.design':
print(x, ...)
lm.design
summary.lm.design

Arguments

formula

for the default method, cf. documentation for lm in package stats;cr for the class design method, a data frame of S3 class

...

further arguments to functions lm, print.lm or print.summary.lm

response

character string giving the name of the response variable (must be among the responses of x; for wide format repeated measurement or parameter designs, response can also be among the column nam

degree

degree for the formula; if NULL, the default for the formula method is used

FUN

function for the aggregate.design method; this must be an unquoted function name; This option is relevant for repeated measurement designs and parameter designs in long format o

use.center

logical indicating whether center points are to be used + in the analysis; relevant for pb and FrF2 designs with center points only; the default FALSE allows application of analysis from package

x

object of class lm or summary.lm, for lm.default like in lm

object

object of class lm.design created by function lm.design

lm.design

a class that is identical in content to class lm; its purpose is to call a specific print method that provides slightly more detail than the standard printout for linear models

summary.lm.design

a class that is identical in content to class summary.lm; its purpose is to call a specific print method that provides slightly more detail than the standard summary for linear models

data

like in lm

subset

like in lm

weights

like in lm

na.action

like in lm

method

like in lm

model

like in lm

y

like in lm

qr

like in lm

singular.ok

like in lm

contrasts

like in lm

offset

like in lm

Value

• The value for the lm functions is a linear model object, exactly like for function lm, except for the added class lm.design. The value of the summary function for class lm.design is a linear model summary, exactly like documented in summary.lm, except for the added class lm.design. The print functions return NULL; they are used for their side effects only.

Details

The lm method for class design conducts 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 2-factor interactions (degree=2, default for most designs) or second order models (that contain quadratic effects in addition to the 2-factor 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 function lm.design itself 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 (method for function lm ) does not reveal enough information, and another pointer that center points have been omitted or repeated measurements aggregated over.

See Also

See also the information on class design and its formula method formula.design

Examples

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))