blm (version 2013.2.4.4)

blm: 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

Value

Returns an object of class blm.

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.

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: http://www.jstatsoft.org/v54/i01/.

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

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

See Also

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

fit <- blm(bladder70~female * smoke_status, 
			      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