lmFAB: FAB inference for linear models

In FABInference: FAB p-Values and Confidence Intervals

Description

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

Usage

lmFAB(cformula, FABvars, lformula = NULL, alpha = 0.05,
  rssSplit = TRUE, 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 linking model (just specify right-hand side)
alpha	error rate for CIs (1-alpha CIs will be constructed)
rssSplit	use some residual degrees of freedom to help fit linking model (TRUE/FALSE)
silent	show progress (TRUE) or not (FALSE)

Value

an object of the class lmFAB which inherits from lm

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<-rnorm(n,lp) 

# fit model
fit<-lmFAB(y~w1+w2,X,~v)

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

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