dreg: Regression for data frames with dutility call

In mets: Analysis of Multivariate Event Times

Regression for data frames with dutility call

Description

Regression for data frames with dutility call

Usage

dreg(
  data,
  y,
  x = NULL,
  z = NULL,
  x.oneatatime = TRUE,
  x.base.names = NULL,
  z.arg = c("clever", "base", "group", "condition"),
  fun. = lm,
  summary. = summary,
  regex = FALSE,
  convert = NULL,
  doSummary = TRUE,
  special = NULL,
  equal = TRUE,
  test = 1,
  ...
)

Arguments

data	data frame
y	name of variable, or fomula, or names of variables on data frame.
x	name of variable, or fomula, or names of variables on data frame.
z	name of variable, or fomula, or names of variables on data frame.
x.oneatatime	x's one at a time
x.base.names	base covarirates
z.arg	what is Z, c("clever","base","group","condition"), clever decides based on type of Z, base means that Z is used as fixed baseline covaraites for all X, group means the analyses is done based on groups of Z, and condition means that Z specifies a condition on the data
fun.	function lm is default
summary.	summary to use
regex	regex
convert	convert
doSummary	doSummary or not
special	special's
equal	to do pairwise stuff
test	development argument
...	Additional arguments for fun

Author(s)

Klaus K. Holst, Thomas Scheike

Examples

##'
data(iris)
dat <- iris
drename(dat) <- ~.
names(dat)
set.seed(1)
dat$time <- runif(nrow(dat))
dat$time1 <- runif(nrow(dat))
dat$status <- rbinom(nrow(dat),1,0.5)
dat$S1 <- with(dat, Surv(time,status))
dat$S2 <- with(dat, Surv(time1,status))
dat$id <- 1:nrow(dat)

mm <- dreg(dat, "*.length"~"*.width"|I(species=="setosa" & status==1))
mm <- dreg(dat, "*.length"~"*.width"|species+status)
mm <- dreg(dat, "*.length"~"*.width"|species)
mm <- dreg(dat, "*.length"~"*.width"|species+status,z.arg="group")

 ## Reduce Ex.Timings
y <- "S*"~"*.width"
xs <- dreg(dat, y, fun.=phreg)
xs <- dreg(dat, y, fun.=survdiff)

y <- "S*"~"*.width"
xs <- dreg(dat, y, x.oneatatime=FALSE, fun.=phreg)

## under condition
y <- S1~"*.width"|I(species=="setosa" & sepal.width>3)
xs <- dreg(dat, y, z.arg="condition", fun.=phreg)
xs <- dreg(dat, y, fun.=phreg)

## under condition
y <- S1~"*.width"|species=="setosa"
xs <- dreg(dat, y, z.arg="condition", fun.=phreg)
xs <- dreg(dat, y, fun.=phreg)

## with baseline  after |
y <- S1~"*.width"|sepal.length
xs <- dreg(dat, y, fun.=phreg)

## by group by species, not working
y <- S1~"*.width"|species
ss <- split(dat, paste(dat$species, dat$status))

xs <- dreg(dat, y, fun.=phreg)

## species as base, species is factor so assumes that this is grouping
y <- S1~"*.width"|species
xs <- dreg(dat, y, z.arg="base", fun.=phreg)

##  background var after | and then one of x's at at time
y <- S1~"*.width"|status+"sepal*"
xs <- dreg(dat, y, fun.=phreg)

##  background var after | and then one of x's at at time
##y <- S1~"*.width"|status+"sepal*"
##xs <- dreg(dat, y, x.oneatatime=FALSE, fun.=phreg)
##xs <- dreg(dat, y, fun.=phreg)

##  background var after | and then one of x's at at time
##y <- S1~"*.width"+factor(species)
##xs <- dreg(dat, y, fun.=phreg)
##xs <- dreg(dat, y, fun.=phreg, x.oneatatime=FALSE)

y <- S1~"*.width"|factor(species)
xs <- dreg(dat, y, z.arg="base", fun.=phreg)

y <- S1~"*.width"|cluster(id)+factor(species)
xs <- dreg(dat, y, z.arg="base", fun.=phreg)
xs <- dreg(dat, y, z.arg="base", fun.=coxph)

## under condition with groups
y <- S1~"*.width"|I(sepal.length>4)
xs <- dreg(subset(dat, species=="setosa"), y,z.arg="group",fun.=phreg)

## under condition with groups
y <- S1~"*.width"+I(log(sepal.length))|I(sepal.length>4)
xs <- dreg(subset(dat, species=="setosa"), y,z.arg="group",fun.=phreg)

y <- S1~"*.width"+I(dcut(sepal.length))|I(sepal.length>4)
xs <- dreg(subset(dat,species=="setosa"), y,z.arg="group",fun.=phreg)

ff <- function(formula,data,...) {
 ss <- survfit(formula,data,...)
 kmplot(ss,...)
 return(ss)
}

if (interactive()) {
dcut(dat) <- ~"*.width"
y <- S1~"*.4"|I(sepal.length>4)
par(mfrow=c(1, 2))
xs <- dreg(dat, y, fun.=ff)
}

mets documentation built on May 29, 2024, 3:51 a.m.