bigReg (version 0.1.5)

bglm: Function to carry out generalized linear regression on a data_frame data object

Description

Function to carry out generalized linear regression on a data_frame data object

Usage

bglm(
  formula,
  family = gaussian_(),
  data,
  weights = NULL,
  offset = NULL,
  start = NULL,
  control = list(),
  etastart = NULL,
  mustart = NULL
)

Arguments

formula

formula that defines your regression model

family

family object from activeReg, e.g. .gaussian(), .binomial(), .poisson(), .quasipoisson(), .quasibinomial(), .Gamma(), .inverse.gaussian(), .quasi()

data

data_frame object containing data for linear regression

weights

weights for the model

offset

offsets for the model

start

starting values for the linear predictor

control

list of parameters for .control() function

etastart

starting values for the linear predictor

mustart

starting values for vector of means

Examples

require(parallel)
data("plasma", package = "bigReg")
data_dir = tempdir()
plasma1 <- plasma
plasma1 <- data_frame(plasma1, 10, path = data_dir, nCores = 1)
plasma_glm <- bglm(ESR ~ fibrinogen + globulin, data = plasma1, family = binomial_("logit"))
summary(plasma_glm)