survMisc (version 0.4.6)

genSurv: Generate survival data

Description

Generate survival data

Usage

genSurvDf(b = 2L, f = 2L, c = 1L, n = 100L, pb = 0.5, nlf = 3L,
  rc = 0.8, pe = 0.5, t0 = 1L, tMax = 100L, asFactor = TRUE,
  model = FALSE, timelim = 5)
genSurvDt(b = 2L, f = 2L, c = 1L, n = 100L, pb = 0.5, nlf = 3L,
  rc = 0.8, pe = 0.5, t0 = 1L, tMax = 100L, asFactor = TRUE,
  model = TRUE, timelim = 5)

Arguments

b

binomial predictors, the number of predictors which are binary, i.e. limited to $0$ or $1$

f

factors, the number of predictors which are factors

c

continuous predictors, the number of predictors which are continuous

n

number of observations (rows) in the data

nlf

the number of levels in a factor

pb

probability for binomnial predictors: the probability of binomial predictors being $=1$ e.g. if pb=0.3, $30%$ will be $1$s, $70%$ will be $0$s

rc

ratio for continuous variables: the ratio of levels of continuous variables to the total number of observations n e.g. if rc=0.8 and n=100, it will be in the range $1-80$

pe

probability of event the probability of events (typically death/failure) occurring, i.e. $P(e=1)$. e.g. if pe=0.6, $60%$ will be $1$s, $40%$ will be $0$s

t0

Lowest (starting) time

tMax

Highest (final) time

asFactor

if asFactor=TRUE (the default), predictors given as factors will be converted to factors in the data frame before the model is fit

timelim

function will timeout after timelim secs. This is present to prevent duplication of rows.

model

If model=TRUE will also return a model fitted with survival::coxph.

Value

• If model=FALSE (the default) a data.frame or data.table as above. If model=TRUE: a list with the following values:
• df or dt
  A data.frame (for genSurvDf) or data.table (for genSurvDt). Predictors are labelled $x1, x2, ..., xn$. Outcome is $t1$ (time) and $e$ event (e.g. death). Rows represent to $n$ observations
• model
  A model fit with survival::coxph

Examples

set.seed(1)
genSurvDf(model=TRUE)
genSurvDf(b=0, c=2, n=100, pe=0.7)
genSurvDf(b=1, c=0, n=1000)
genSurvDf(f=1, nlf=4, b=1, c=0, asFactor=FALSE)
set.seed(1)
genSurvDt()
genSurvDt(b=0, f=0, c=1, n=20L, pe=0.7)