mets (version 1.2.3.1)

dreg: 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,
  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

fun.

function

summary.

summary to use

regex

regex

convert

convert

special

special's

equal

to do pairwise stuff

test

development argument

...

Additional arguments for fun

Examples

# NOT RUN {
data(iris)
data=iris
drename(iris) <- ~.
names(iris)
iris$time <- runif(nrow(iris))
iris$time1 <- runif(nrow(iris))
iris$status <- rbinom(nrow(iris),1,0.5)
iris$S1 <- with(iris,Surv(time,status))
iris$S2 <- with(iris,Surv(time1,status))
iris$id <- 1:nrow(iris)

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

# }
# NOT RUN {
 ## Reduce Ex.Timings
y <- "S*"~"*.width"
xs <- dreg(iris,y,fun.=phreg)
xs <- dreg(iris,y,fun.=survdiff)

### testing forskellige calls
y <- "S*"~"*.width"
xs <- dreg(iris,y,x.oneatatime=FALSE,fun.=phreg)

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

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

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

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


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

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

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

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

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


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

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

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

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

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