Publish (version 2023.01.17)

specialFrame: Special frame

Description

Extract data and design matrix including specials from call

Usage

specialFrame(
  formula,
  data,
  unspecials.design = TRUE,
  specials,
  specials.factor = TRUE,
  specials.design = FALSE,
  strip.specials = TRUE,
  strip.arguments = NULL,
  strip.alias = NULL,
  strip.unspecials = NULL,
  drop.intercept = TRUE,
  response = TRUE,
  na.action = options()$na.action
)

Value

A list which contains - the response - the design matrix (see model.design) - one entry for each special (see model.design)

Arguments

formula

Formula whose left hand side specifies the event history, i.e., either via Surv() or Hist().

data

Data frame in which the formula is interpreted

unspecials.design

Passed as is to model.design.

specials

Character vector of special function names. Usually the body of the special functions is function(x)x but e.g., strata from the survival package does treat the values

specials.factor

Passed as is to model.design.

specials.design

Passed as is to model.design

strip.specials

Passed as specials to strip.terms

strip.arguments

Passed as arguments to strip.terms

strip.alias

Passed as alias.names to strip.terms

strip.unspecials

Passed as unspecials to strip.terms

drop.intercept

Passed as is to model.design

response

If FALSE do not get response data.

na.action

Decide what to do with missing values.

Author

Thomas A. Gerds <[e-mail]>

Details

Obtain a list with the data used for event history regression analysis. This function cannot be used directly on the user level but inside a function to prepare data for survival analysis.

See Also

model.frame model.design Hist

Examples

## Here are some data with an event time and no competing risks
## and two covariates X1 and X2.
## Suppose we want to declare that variable X1 is treated differently
## than variable X2. For example, X1 could be a cluster variable, or
## X1 should have a proportional effect on the outcome.
d <- data.frame(y=1:7,
                X2=c(2.24,3.22,9.59,4.4,3.54,6.81,5.05),
                X3=c(1,1,1,1,0,0,1),
                X4=c(44.69,37.41,68.54,38.85,35.9,27.02,41.84),
                X1=factor(c("a","b","a","c","c","a","b"),
                    levels=c("c","a","b")))
## define special functions prop and cluster
prop <- function(x)x
cluster <- function(x)x
## We pass a formula and the data
e <- specialFrame(y~prop(X1)+X2+cluster(X3)+X4,
                  data=d,
                  specials=c("prop","cluster"))
## The first element is the response
e$response
## The other elements are the design, i.e., model.matrix for the non-special covariates
e$design
## and a data.frame for the special covariates
e$prop
## The special covariates can be returned as a model.matrix 
e2 <- specialFrame(y~prop(X1)+X2+cluster(X3)+X4,
                   data=d,
                   specials=c("prop","cluster"),
                   specials.design=TRUE)
e2$prop
## and the non-special covariates can be returned as a data.frame
e3 <- specialFrame(y~prop(X1)+X2+cluster(X3)+X4,
                   data=d,
                   specials=c("prop","cluster"),
                   specials.design=TRUE,
                   unspecials.design=FALSE)
e3$design