blm {blm} R Documentation

## Fit a binomial linear regression model

### Description

A direct probability model for regression with a binary outcome from observational data.

### Usage

blm(formula, data, na.action = na.omit, weights = NULL,
strata = NULL, par.init = NULL, warn=FALSE,...)


### Arguments

 formula formula for linear model for binary outcome, event~x1+x2+... data data.frame containing the variables of formula na.action function specifying how missing data should be handled, na.action weights Vector of weights equal to the number of observations. For population-based case-control study, weights are the inverse sampling fractions for controls. strata vector indicating the stratification for weighted regression with stratified observational data par.init vector (optional) of initial parameters warn logical indicator whether to include warnings during algorithm fitting. Default of FALSE suppresses warnings when testing for feasible parameters. ... Additional arguments passed to constrOptim

### Details

The blm model coefficients are the solutions to the maximum of a pseudo log-likelihood using a constrained optimization algorithm with an adaptive barrier method, constrOptim (Lange, 2010). Variance estimates are based on Taylor linearization (Shah, 2002). When weights are not NULL, it is assumed that the study is a case-control design.

### Value

Returns an object of class blm.

### Author(s)

S. Kovalchik s.a.kovalchik@gmail.com

### References

Kovalchik S, Varadhan R (2013). Fitting Additive Binomial Regression Models with the R Package blm. Journal of Statistical Software, 54(1), 1-18. URL: https://www.jstatsoft.org/v54/i01/.

Lange, K. (2010) Numerical Analysis for Statisticians, Springer.

Shah, BV. (2002) Calculus of Taylor deviations. Joint Statistical Meetings.

constrOptim

### Examples


data(ccdata)

fit <- blm(y~female+packyear, weights = ccdata$w,strata=ccdata$strata,
data=ccdata)

summary(fit)

data(aarp)

# ABSOLUTE RISK OF BLADDER CANCER BY 70 YEARS
# FOR DIFFERENT GENDER AND RISK GROUP

data = aarp,
weight=aarp\$w)

logLik(fit)

# INTERCEPT IS BASELINE RISK
# ALL OTHER COEFFICIENTS ARE RISK DIFFERENCES FROM BASELINE

summary(fit)

# RISK DIFFERENCE CONFIDENCE INTERVALS (PER 1,000 PERSONS)
confint(fit)*1000


[Package blm version 2022.0.0.1 Index]