Regression for data frames with dutility call

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

Klaus K. Holst, Thomas Scheike

Examples

##'
data(iris)
dat <- iris
drename(dat) <- ~.
names(dat)
#> [1] "sepal.length" "sepal.width"  "petal.length" "petal.width"  "species"     
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)
}