| Title: | Computes Bias-Adjusted Treatment Effect |
|---|---|
| Description: | Compute bounds for the treatment effect after adjusting for the presence of omitted variables in linear econometric models, according to the method of Basu (2022) <arXiv:2203.12431>. You supply the data, identify the outcome and treatment variables and additional regressors. The main functions will compute bounds for the bias-adjusted treatment effect. Many plot functions allow easy visualization of results. |
| Authors: | Deepankar Basu [aut, cre], Evan Wasner [aut] |
| Maintainer: | Deepankar Basu <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.0 |
| Built: | 2025-01-23 03:11:28 UTC |
| Source: | https://github.com/dbasu-umass/bate |
Conduct partial and total R2-based sensitivity analysis
cinhaz(kd, ky, data, outcome, treatment, bnch_reg, other_reg, alpha)cinhaz(kd, ky, data, outcome, treatment, bnch_reg, other_reg, alpha)
kd |
relative strength of the confounder in explaining variation in treatment as compared to benchmark covariate(s) |
ky |
relative strength of confounder in explaining variation in outcome as compared to benchmerk covariate(s) |
data |
data frame |
outcome |
outcome variable |
treatment |
treatment variable |
bnch_reg |
benchmark covariate(s) |
other_reg |
other covariates in the model (other than treatment and benchmark covariates) |
alpha |
significance level for hypothesis test (H0: true effect = 0) |
A data frame with results
## Load library library(sensemakr) ## Conduct analysis cinhaz(kd=1,ky=1,data=darfur,outcome = "peacefactor", treatment = "directlyharmed", bnch_reg = "female", other_reg = c("village","age","farmer_dar","herder_dar","pastvoted","hhsize_darfur"), alpha=0.05)## Load library library(sensemakr) ## Conduct analysis cinhaz(kd=1,ky=1,data=darfur,outcome = "peacefactor", treatment = "directlyharmed", bnch_reg = "female", other_reg = c("village","age","farmer_dar","herder_dar","pastvoted","hhsize_darfur"), alpha=0.05)
Collect parameters from the short, intermediate and auxiliary regressions
collect_par(data, outcome, treatment, control, other_regressors = NULL)collect_par(data, outcome, treatment, control, other_regressors = NULL)
data |
A data frame. |
outcome |
The name of the outcome variable (must be present in the data frame). |
treatment |
The name of the treatment variable (must be present in the data frame). |
control |
Control variables to be added to the intermediate regression. |
other_regressors |
Subset of control variables to be added in the short regression (default is NULL). |
A data frame with the following columns:
beta0 |
Treatment effect in the short regression |
R0 |
R-squared in the short regression |
betatilde |
Treatment effect in the intermediate regression |
Rtilde |
R-squared in the intermediate regression |
sigmay |
Standard deviation of outcome variable |
sigmax |
Standard deviation of treatment variable |
taux |
Standard deviation of residual in auxiliary regression |
## 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")) ## See results (parameters)## 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")) ## See results (parameters)
Create contour plot of bias
cplotbias(data)cplotbias(data)
data |
A data frame that is the output from the "ovbias" function. |
A plot object created with ggplot
## 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$Rtilde Rhigh <- 0.61 deltalow <- 0.01 deltahigh <- 0.99 e <- 0.01 ## Not run: ## Compute bias and bias-adjusted treatment effect OVB <- ovbias( parameters = parameters, deltalow=deltalow, deltahigh=deltahigh, Rhigh=Rhigh, e=e) ## Contour Plot of bias over the bounded box p2 <- cplotbias(OVB$Data) print(p2) ## End(Not run)## 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$Rtilde Rhigh <- 0.61 deltalow <- 0.01 deltahigh <- 0.99 e <- 0.01 ## Not run: ## Compute bias and bias-adjusted treatment effect OVB <- ovbias( parameters = parameters, deltalow=deltalow, deltahigh=deltahigh, Rhigh=Rhigh, e=e) ## Contour Plot of bias over the bounded box p2 <- cplotbias(OVB$Data) print(p2) ## End(Not run)
Plot graph of function delta=f(Rmax)
delfplot(parameters)delfplot(parameters)
parameters |
A vector of parameters that is generated after estimating the short, intermediate and auxiliary regressions. |
A plot object created with ggplot
## 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$Rtilde Rhigh <- 0.61 deltalow <- 0.01 deltahigh <- 0.99 e <- 0.01 ## Oster's method: Plot of delta = f(Rmax) p4 <- delfplot(parameters = parameters) print(p4)## Load data set data("NLSY_IQ") ## Set age and race as factor variables NLSY_IQ$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 ## Oster's method: Plot of delta = f(Rmax) p4 <- delfplot(parameters = parameters) print(p4)
Histogram of bias adjusted treatment effect
dplotbate(data)dplotbate(data)
data |
A data frame that is the output from the "ovbias" function. |
A plot object created with ggplot
## 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$Rtilde Rhigh <- 0.61 deltalow <- 0.01 deltahigh <- 0.99 e <- 0.01 ## Not run: ## Compute bias and bias-adjusted treatment effect OVB <- ovbias( parameters = parameters, deltalow=deltalow, deltahigh=deltahigh, Rhigh=Rhigh, e=e) ## Histogram and density Plot of bstar distribution p3 <- dplotbate(OVB$Data) print(p3) ## End(Not run)## 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$Rtilde Rhigh <- 0.61 deltalow <- 0.01 deltahigh <- 0.99 e <- 0.01 ## Not run: ## Compute bias and bias-adjusted treatment effect OVB <- ovbias( parameters = parameters, deltalow=deltalow, deltahigh=deltahigh, Rhigh=Rhigh, e=e) ## Histogram and density Plot of bstar distribution p3 <- dplotbate(OVB$Data) print(p3) ## End(Not run)
Extend border of bounded box by +/- e
expand_border(parameters, deltalow, deltahigh, Rlow, Rhigh, e)expand_border(parameters, deltalow, deltahigh, Rlow, 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. |
Rlow |
The lower limit of Rmax. |
Rhigh |
The upper limit of Rmax. |
e |
The step size. |
Data frame.
Identify all border points in a region
get_border(region, e)get_border(region, e)
region |
A data frame containing the x and y coordinates of the region. |
e |
The step size of the grid in the x and y directions. |
A data frame containing the x and y coordinates of the border points of the region.
Compute roots of the cubic equation
mycubic(parameters, mydelta, Rmax)mycubic(parameters, mydelta, Rmax)
parameters |
A vector of parameters (real numbers) that is generated by estimating the short, intermediate and auxiliary regressions. |
mydelta |
Value of delta (real number). |
Rmax |
Value of Rmax (real number). |
A vector containing the three roots of the cubic equation defined by the parameters, delta and Rmax.
Evaluates discriminant of the cubic equation
mydisc(parameters, mydelta, Rmax)mydisc(parameters, mydelta, Rmax)
parameters |
A vector of parameters (real numbers) that is generated by estimating the short, intermediate and auxiliary regressions. |
mydelta |
The value of delta (real number). |
Rmax |
The value of Rmax (real number) |
Returns a value of 0 or 1; 0 (if discriminant is positive) and 1 (if discriminant is nonpositive)
NLSY data to analyse the effect of maternal behaviour on children's birth weight. Natality detail files are from 2001 and 2002. Data is from the NLSY Children and Young Adults panel.
NLSY_BWNLSY_BW
A data frame with 7686 observations on 13 variables:
birth_wtbirth weight, grams
BF_monthsmonths of breast feeding
mom_drink_preg_alldid the mother drink at all during pregnancy
lbw_pretermlow birth weight + preterm
ageage of child
femalechild female
blackmother black
motherAgeage of mother
motherEDUyears of schooling of mother
mom_marriedis the mother married?
incomeannual income of mother
sexyears of schooling of mother
racerace of mother
gesweekgestation week
any_smokedid the mother smoke at all during pregnancy
Source: https://drive.google.com/file/d/1O1W9dP8F3B1DnAZGBegpoqCfysUrn7Uc/view?usp=sharing
## Load data set data("NLSY_BW") ## See names of variables names(NLSY_BW)## Load data set data("NLSY_BW") ## See names of variables names(NLSY_BW)
NLSY data to analyse the effect of maternal behaviour on children's IQ score. Natality detail files are from 2001 and 2002. Data is from the NLSY Children and Young Adults panel.
NLSY_IQNLSY_IQ
A data frame with 6514 observations on 13 variables:
iq_stdstandardized IQ score, PIAT score
BF_monthsmonths of breast feeding
mom_drink_preg_alldid mother drink at all during pregnancy
lbw_pretermlow birth weight + preterm
ageage of child
femalechild female
blackmother black
motherAgeage of mother
motherEDUyears of schooling of mother
mom_marriedis the mother married?
incomeannual income of mother
sexchild sex
racerace of mother
Source: https://drive.google.com/file/d/1O1W9dP8F3B1DnAZGBegpoqCfysUrn7Uc/view?usp=sharing
## Load data set data("NLSY_IQ") ## See names of variables names(NLSY_IQ)## Load data set data("NLSY_IQ") ## See names of variables names(NLSY_IQ)
Computes identified set according to Oster (2019)
osterbds(parameters, Rmax)osterbds(parameters, Rmax)
parameters |
A vector of parameters that is generated after estimating the short, intermediate and auxiliary regressions. |
Rmax |
A real number which lies between Rtilde (R-squared for the intermediate regression) and 1. |
A data frame with three columns:
Discriminant |
The value of the discriminant of the quadratic equation that is solved to generate the identified set |
Interval1 |
The interval formed with the first root of the quadratic equation |
Interval2 |
The interval formed with the first root of the quadratic equation |
## 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")) ## Oster's method: bounding sets when Rmax=0.61 osterbds(parameters = parameters, Rmax=0.61)## 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")) ## Oster's method: bounding sets when Rmax=0.61 osterbds(parameters = parameters, Rmax=0.61)
Computes delta* according to Oster (2019)
osterdelstar(parameters, Rmax)osterdelstar(parameters, Rmax)
parameters |
A vector of parameters that is generated after estimating the short, intermediate and auxiliary regressions. |
Rmax |
A real number that lies between Rtilde (R-squared for the intermediate regression) and 1. |
A data frame with three columns:
delstar |
The value of delta for the chosen value of Rmax |
discontinuity |
Indicates whether the point of discontinuity is within the interval formed by Rtilde and 1 |
slope |
Slope of the function, delta=f(Rmax) |
## 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")) ## Oster's method: delta* (for Rmax=0.61) osterdelstar(parameters = parameters, Rmax=0.61)## 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")) ## Oster's method: delta* (for Rmax=0.61) osterdelstar(parameters = parameters, Rmax=0.61)
Compute bias adjusted treatment effect taking parameter vector as input.
ovbias(parameters, deltalow, deltahigh, Rhigh, e)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$bstar, c(0.01,0.05,0.1,0.9,0.95,0.975)) ## End(Not run)## Load data set data("NLSY_IQ") ## Set age and race as factor variables NLSY_IQ$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)
Compute bias adjusted treatment effect taking three lm objects as input.
ovbias_lm(lm_shrt, lm_int, lm_aux, deltalow, deltahigh, Rhigh, e)ovbias_lm(lm_shrt, lm_int, lm_aux, deltalow, deltahigh, Rhigh, e)
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 |
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 |
## 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$bstar_Distribution ## End(Not run)## Load data set data("NLSY_IQ") ## Set age and race as factor variables NLSY_IQ$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)
Compute bias adjusted treatment effect taking data frame as input.
ovbias_par( data, outcome, treatment, control, other_regressors = NULL, deltalow, deltahigh, Rhigh, e )ovbias_par( data, outcome, treatment, control, other_regressors = NULL, deltalow, deltahigh, Rhigh, e )
data |
Data frame. |
outcome |
Outcome variable. |
treatment |
Treatment variable. |
control |
Control variables to add in the intermediate regression. |
other_regressors |
Subset of control variables to add in the short regression (default is NULL). |
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 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 |
## Load data set data("NLSY_IQ") ## Set parameters for bounded box Rhigh <- 0.61 deltalow <- 0.01 deltahigh <- 0.99 e <- 0.01 ## Not run: ## Compute bias and bias-adjusted treatment effect OVB_par <- ovbias_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"), deltalow=deltalow, deltahigh=deltahigh, Rhigh=Rhigh, e=e) ## Default quantiles of bias OVB_par$bias_Distribution # Default quantiles of bias-adjusted treatment effect OVB_par$bstar_Distribution ## End(Not run)## Load data set data("NLSY_IQ") ## Set parameters for bounded box Rhigh <- 0.61 deltalow <- 0.01 deltahigh <- 0.99 e <- 0.01 ## Not run: ## Compute bias and bias-adjusted treatment effect OVB_par <- ovbias_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"), deltalow=deltalow, deltahigh=deltahigh, Rhigh=Rhigh, e=e) ## Default quantiles of bias OVB_par$bias_Distribution # Default quantiles of bias-adjusted treatment effect OVB_par$bstar_Distribution ## End(Not run)
Returns coefficients of the cubic equation
partocoef(parameters, mydelta, Rmax)partocoef(parameters, mydelta, Rmax)
parameters |
A vector of parameters (real numbers) that is generated by estimating the short, intermediate and auxiliary regressions. |
mydelta |
The value of delta (real number) |
Rmax |
The value of Rmax (real number) |
A data frame with the coefficients of the cubic equation.
Select root of the cubic based on the root of a nearest point
selectroot(parameters, mydelta, Rmax, closest_bias)selectroot(parameters, mydelta, Rmax, closest_bias)
parameters |
A vector of parameters (real numbers) that is generated by estimating the short, intermediate and auxiliary regressions. |
mydelta |
The value of delta (real number). |
Rmax |
The value of Rmax (real number). |
closest_bias |
The value of bias at the nearest point. |
Data frame
Split a region into two parts
split_nurr(region1, region2, epsilon, parameters, e)split_nurr(region1, region2, epsilon, parameters, e)
region1 |
Data frame with coordinates for region 1 |
region2 |
Data frame with coordinates for region 2 |
epsilon |
Closest distance |
parameters |
A vector of parameters (real numbers) that is generated by estimating the short, intermediate and auxiliary regressions. |
e |
The step size of the grid in the x and y directions. |
List, where first element is region within epsilon distance of region 1 and second element which is region which is not within epsilon distance of region 1.
Region plot to demarcate URR and NURR for the bounded box
urrplot(parameters, deltalow, deltahigh, Rlow, Rhigh, e)urrplot(parameters, deltalow, deltahigh, Rlow, Rhigh, e)
parameters |
A vector of parameters (real numbers) that is generated by estimating the short, intermediate and auxiliary regressions. |
deltalow |
The lower limit for delta. |
deltahigh |
The upper limit for delta. |
Rlow |
The lower limit for Rmax. |
Rhigh |
The upper limit for Rmax. |
e |
The step size of the grid in the x and y directions. |
A plot object created by ggplot
## 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$Rtilde Rhigh <- 0.61 deltalow <- 0.01 deltahigh <- 0.99 e <- 0.01 ## Create region plot for bounded box p1 <- urrplot(parameters, deltalow, deltahigh, Rlow, Rhigh, e=e) ## See plot print(p1)## Load data set data("NLSY_IQ") ## Set age and race as factor variables NLSY_IQ$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 ## Create region plot for bounded box p1 <- urrplot(parameters, deltalow, deltahigh, Rlow, Rhigh, e=e) ## See plot print(p1)