ovbias_lm {bate} R Documentation

## Compute bias adjusted treatment effect taking three lm objects as input.

### Description

Compute bias adjusted treatment effect taking three lm objects as input.

### Usage

ovbias_lm(lm_shrt, lm_int, lm_aux, deltalow, deltahigh, Rhigh, e)


### Arguments

 lm_shrt lm object corresponding to the short regression lm_int lm object corresponding to the intermediate regression lm_aux lm object corresponding to the auxiliary regression deltalow The lower limit of delta deltahigh The upper limit of delta Rhigh The upper limit of Rmax e The step size

### Value

List with three elements:

 Data Data frame containing the bias and bias-adjusted treatment effect for each point on the grid bias_Distribution Quantiles (2.5,5.0,50,95,97.5) of the empirical distribution of bias bstar_Distribution Quantiles (2.5,5.0,50,95,97.5) of the empirical distribution of the bias-adjusted treatment effect

### Examples

## Load data set
data("NLSY_IQ")

## Set age and race as factor variables
NLSY_IQ$age <- factor(NLSY_IQ$age)
NLSY_IQ$race <- factor(NLSY_IQ$race)

## Short regression
reg_s <- lm(iq_std ~ BF_months + factor(age) + sex, data = NLSY_IQ)

## Intermediate regression
reg_i <- lm(iq_std ~ BF_months +
factor(age) + sex + income + motherAge +
motherEDU + mom_married + factor(race),
data = NLSY_IQ)

## Auxiliary regression
reg_a <- lm(BF_months ~ factor(age) +
sex + income + motherAge + motherEDU +
mom_married + factor(race), data = NLSY_IQ)

## Set limits for the bounded box
Rlow <- summary(reg_i)$r.squared Rhigh <- 0.61 deltalow <- 0.01 deltahigh <- 0.99 e <- 0.01 ## Not run: ## Compute bias and bias-adjusted treatment effect ovb_lm <- ovbias_lm(lm_shrt = reg_s,lm_int = reg_i, lm_aux = reg_a, deltalow=deltalow, deltahigh=deltahigh, Rhigh=Rhigh, e=e) ## Default quantiles of bias ovb_lm$bias_Distribution

# Default quantiles of bias-adjusted treatment effect
ovb_lm\$bstar_Distribution

## End(Not run)



[Package bate version 0.1.0 Index]