ovbias_lm function

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

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

Returns

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)