glmFAB: FAB inference for generalized linear models

In FABInference: FAB p-Values and Confidence Intervals

Description

asymptotic FAB p-values and confidence intervals for parameters in generalized linear regression models

Usage

glmFAB(cformula, FABvars, lformula = NULL, alpha = 0.05,
  silent = FALSE, ...)

Arguments

cformula	formua for the control variables
FABvars	matrix of regressors for which to make FAB p-values and CIs
lformula	formula for the lining model (just specify right-hand side)
alpha	error rate for CIs (1-alpha CIs will be constructed)
silent	show progress (TRUE) or not (FALSE)
...	additional arguments to be passed to glm

Value

an object of the class glmFAB which inherits from glm

Author(s)

Peter Hoff

Examples

# n observations, p FAB variables, q=2 control variables 

n<-100 ; p<-25 

# X is design matrix for params of interest
# beta is vector of true parameter values 
# v a variable in the linking model - used to share info across betas

v<-rnorm(p) ; beta<-(2 - 2*v + rnorm(p))/3 ; X<-matrix(rnorm(n*p),n,p)/8

# control coefficients and variables  
alpha1<-.5 ; alpha2<- -.5
w1<-rnorm(n)/8
w2<-rnorm(n)/8

# simulate data 
lp<-1 + alpha1*w1 + alpha2*w2 + X%*%beta 
y<-rpois(n,exp(lp))

# fit model
fit<-glmFAB(y~w1+w2,X,~v,family=poisson)

fit$FABpv
fit$FABci 
summary(fit) # look at p-value column 

FABInference documentation built on Jan. 9, 2020, 5:08 p.m.