ovbias {bate} | R Documentation |
Compute bias adjusted treatment effect taking parameter vector as input.
ovbias(parameters, deltalow, deltahigh, Rhigh, e)
parameters |
A vector of parameters (real numbers) that is generated by estimating the short, intermediate and auxiliary regressions. |
deltalow |
The lower limit of delta. |
deltahigh |
The upper limit of delta. |
Rhigh |
The upper limit of Rmax. |
e |
The step size. |
List with three elements:
Data |
Data frame containing the bias ($bias) and bias-adjusted treatment effect ($bstar) 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 |
## 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)
## Collect parameters from the short, intermediate and auxiliary regressions
parameters <- collect_par(
data = NLSY_IQ, outcome = "iq_std",
treatment = "BF_months",
control = c("age","sex","income","motherAge","motherEDU","mom_married","race"),
other_regressors = c("sex","age"))
## Set limits for the bounded box
Rlow <- parameters$Rtilde
Rhigh <- 0.61
deltalow <- 0.01
deltahigh <- 0.99
e <- 0.01
## Not run:
## Compute bias and bias-adjusted treatment effect
OVB <- ovbias(
parameters = parameters,
deltalow=deltalow,
deltahigh=deltahigh, Rhigh=Rhigh,
e=e)
## Default quantiles of bias
(OVB$bias_Distribution)
## Chosen quantilesof bias
quantile(OVB$Data$bias, c(0.01,0.05,0.1,0.9,0.95,0.975))
## Default quantiles of bias-adjusted treatment effect
(OVB$bstar_Distribution)
## Chosen quantiles of bias-adjusted treatment effect
quantile(OVB$Data$bstar, c(0.01,0.05,0.1,0.9,0.95,0.975))
## End(Not run)