blm {blm} | R Documentation |
A direct probability model for regression with a binary outcome from observational data.
blm(formula, data, na.action = na.omit, weights = NULL,
strata = NULL, par.init = NULL, warn=FALSE,...)
formula |
formula for linear model for binary outcome, |
data |
data.frame containing the variables of |
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 |
... |
Additional arguments passed to |
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.
Returns an object of class blm
.
S. Kovalchik s.a.kovalchik@gmail.com
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.
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