blm (version 1.2)

blm: Fit a binomial linear regression model

Description

Binomial linear model for cohort binary data.

Usage

blm(f,data,par.init,ineq=NULL,trace=FALSE,tol=1e-6,adaptive=FALSE,...)

Arguments

f

formula for linear model for binary outcome, event~x1+x2+...

data

data.frame containing the variables of f

par.init

optional vector of user-specified initial parameters

ineq

optional user-specified inequality constraint matrix.

trace

logical, whether details of optimization should be printed with each outer iteration.

tol

numeric criterion for judging a constraint active.

adaptive

logical, whether the optimization method should use an adpative barrier.

...

Additional argument to control.outer of constrOptim.nl

Value

• Returns an object of class blm.

Details

blm is a wrapper for constrOptim.nl which performs constrained optimization for nonlinear functions and linera/nonlinear inequality constraints.

When par.init is not specified, a linear projection of the OLS estimators into the feasible region are used.

The ineq matrix is determined automatically from the unique covariate patterns of the design matrix containing only the extreme values (endpoints of empirical range) for each column of continuous variables. It can also be supplied directly by the user. In this case, it is a matrix with the rows equal to the number of inequality constraints for the lower bound and the columns corresponding to each column of the model design matrix.

When a constraint is at the boundary of the feasible region, the constraint will be approximately zero when evaluated at the final estimates of the algorithm. tol is the criterion for zero, so any evaluated contraints less than or equal to tol are considered active. If any constraints are active, the covariance-variance based on the Taylor series approximation might be inaccurate.

See Also

blm-class, constrOptim.nl

Examples

data(grad)

fit <- blm(admit~factor(rank),grad)

fit 

summary(fit)

ci(fit,c(1,0,0,0))  #PROB GRAD SCHOOL ADMISSION 
		    #FOR STUDENTS FROM MOST PRESTIGIOUS SCHOOL


### INCLUDE FACTORS FOR UNDERGRAD GPA AND GRE AND SUPPLY INITIAL VALUES

fit2 <- blm(admit~gre+gpa+factor(rank),grad,par.init=c(.5,rep(0,5)))

fit2

### IMPROVEMENT IN AIC USING 2 DEGREES OF FREEDOM

summary(fit)$AIC-summary(fit2)$AIC