LogisticDx (version 0.3)

genBinom: Generate data for binomial regression

Description

Generates a data.frame or data.table with a binary outcome, and a logistic model to describe it.

Usage

genBinomDf(
  b = 2L,
  f = 2L,
  c = 1L,
  n = 20L,
  nlf = 3L,
  pb = 0.5,
  rc = 0.8,
  py = 0.5,
  asFactor = TRUE,
  model = FALSE,
  timelim = 5,
  speedglm = FALSE
)
genBinomDt(
  b = 2L,
  f = 2L,
  c = 1L,
  n = 20L,
  nlf = 3L,
  pb = 0.5,
  rc = 0.8,
  py = 0.5,
  asFactor = TRUE,
  model = FALSE,
  timelim = 5,
  speedglm = FALSE
)

Arguments

b

The number of binomial variables (the number of predictors which are binary).

These are limited to \(0\) or \(1\).

f

The number of factor predictors.

The number of predictors which are factors.

c

The number of continuous predictors.

the number of predictors which are continuous.

n

The number of observations (rows) in the data.frame or data.table.

nlf

The number of levels in a factor.

pb

The 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

The 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 \(1\) to \(80\).

py

The ratio for y, the ratio of \(1\)s to the total number of observations for the binomial predictors.

E.g. if ry=0.5, 50% will be \(1\)s, \(50\%\) will be \(0\)s.

asFactor

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

model

If model=TRUE, will also return a model fitted with stats::glm or speedglm::speedglm

timelim

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

speedglm

If speedglm=TRUE, return a model fitted with speedglm instead of glm. See: ?speedglm::speedglm

Value

If model=TRUE: a list with the following values:

df or dt

A data.frame (for genBinomDf) or data.table (for genBinomDt).

Predictors are labelled \(x1, x2, ..., xn\).

The response is \(y\).

Rows represent to \(n\) observations

model

A model fit with stats::glm or speedglm::speedglm

If model=FALSE a data.frame or data.table as above.

Examples

# NOT RUN {
set.seed(1)
genBinomDf(speedglm=TRUE)

genBinomDt(b=0, c=2, n=100L, rc=0.7, model=FALSE)

# }