model.design | R Documentation |
Extract design matrix and data specials from a model.frame
model.design(
terms,
data,
xlev = NULL,
dropIntercept = FALSE,
maxOrder = 1,
unspecialsDesign = TRUE,
specialsFactor = FALSE,
specialsDesign = FALSE
)
terms |
terms object as obtained either with function |
data |
A data set in which terms are defined. |
xlev |
a named list of character vectors giving the full set of levels to be assumed for the factors.
Can have less elements, in which case the other levels are learned from the |
dropIntercept |
If TRUE drop intercept term from the design matrix |
maxOrder |
An error is produced if special variables are involved in interaction terms of order higher than max.order. |
unspecialsDesign |
A logical value: if |
specialsFactor |
A character vector containing special
variables which should be coerced into a single factor. If
|
specialsDesign |
A character vector containing special
variables which should be transformed into a design matrix via
|
The function separates special terms from the unspecial terms and returns
a list of design matrices, one for unspecial terms and one for each special.
Some special specials cannot or should not be evaluated in
data. E.g., y~a+dummy(x)+strata(v)
the function strata can and should be evaluated,
but in order to have model.frame
also evaluate dummy(x) one would be to define
and export the function dummy
. Still the term dummy(x)
can be used
to identify a special treatment of the variable x
. To deal with this case,
one can specify stripSpecials="dummy"
. In addition, the data
should include variables strata(z)
and x
, not dummy(x)
.
See examples.
The function untangle.specials
of the survival function does a similar job.
A list which contains - the design matrix with the levels of the variables stored in attribute 'levels' - separate data.frames which contain the values of the special variables.
Thomas A. Gerds <tag@biostat.ku.dk>
EventHistory.frame
model.frame terms model.matrix .getXlevels
# specials that are evaluated. here ID needs to be defined
set.seed(8)
d <- data.frame(y=rnorm(5),x=factor(c("a","b","b","a","c")),z=c(2,2,7,7,7),v=sample(letters)[1:5])
d$z <- factor(d$z,levels=c(1:8))
ID <- function(x)x
f <- formula(y~x+ID(z))
t <- terms(f,special="ID",data=d)
mda <- model.design(terms(t),data=d,specialsFactor=TRUE)
mda$ID
mda$design
##
mdb <- model.design(terms(t),data=d,specialsFactor=TRUE,unspecialsDesign=FALSE)
mdb$ID
mdb$design
# set x-levels
attr(mdb$ID,"levels")
attr(model.design(terms(t),data=d,xlev=list("ID(z)"=1:10),
specialsFactor=TRUE)$ID,"levels")
# special specials (avoid define function SP)
f <- formula(y~x+SP(z)+factor(v))
t <- terms(f,specials="SP",data=d)
st <- strip.terms(t,specials="SP",arguments=NULL)
md2a <- model.design(st,data=d,specialsFactor=TRUE,specialsDesign="SP")
md2a$SP
md2b <- model.design(st,data=d,specialsFactor=TRUE,specialsDesign=FALSE)
md2b$SP
# special function with argument
f2 <- formula(y~x+treat(z,power=2)+treat(v,power=-1))
t2 <- terms(f2,special="treat")
st2 <- strip.terms(t2,specials="treat",arguments=list("treat"=list("power")))
model.design(st2,data=d,specialsFactor=FALSE)
model.design(st2,data=d,specialsFactor=TRUE)
model.design(st2,data=d,specialsDesign=TRUE)
library(survival)
data(pbc)
t3 <- terms(Surv(time,status!=0)~factor(edema)*age+strata(I(log(bili)>1))+strata(sex),
specials=c("strata","cluster"))
st3 <- strip.terms(t3,specials=c("strata"),arguments=NULL)
md3 <- model.design(terms=st3,data=pbc[1:4,])
md3$strata
md3$cluster
f4 <- Surv(time,status)~age+const(factor(edema))+strata(sex,test=0)+prop(bili,power=1)+tp(albumin)
t4 <- terms(f4,specials=c("prop","timevar","strata","tp","const"))
st4 <- strip.terms(t4,
specials=c("prop","timevar"),
unspecials="prop",
alias.names=list("timevar"="strata","prop"=c("const","tp")),
arguments=list("prop"=list("power"=0),"timevar"=list("test"=0)))
formula(st4)
md4 <- model.design(st4,data=pbc[1:4,],specialsDesign=TRUE)
md4$prop
md4$timevar
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