LogisticDx (version 0.1)

genLogi: Generate data for logistic regression

Description

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

Usage

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

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

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 in the data frame

nlf

the no. 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

py

ratio for y the ratio of 1s to total 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

Value

• If model=TRUE: a list with the following values:
• df or dt
  A data.frame (for genLogiDf) or data.table (for genLogiDt). Predictors are labelled $x1, x2, ..., xn$. Outcome 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

set.seed(1)
genLogiDf()
genLogiDt(b=0, c=2, n=100, rc=0.7, model=FALSE)