NLSY Birth Weight.

Description

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.

Usage

NLSY_BW

Format

A data frame with 7686 observations on 13 variables:

birth_wt

birth weight, grams

BF_months

months of breast feeding

mom_drink_preg_all

did the mother drink at all during pregnancy

lbw_preterm

low birth weight + preterm

age

age of child

female

child female

black

mother black

motherAge

age of mother

motherEDU

years of schooling of mother

mom_married

is the mother married?

income

annual income of mother

sex

years of schooling of mother

race

race of mother

gesweek

gestation week

any_smoke

did the mother smoke at all during pregnancy

Source: https://drive.google.com/file/d/1O1W9dP8F3B1DnAZGBegpoqCfysUrn7Uc/view?usp=sharing

Examples

## Load data set
data("NLSY_BW")
## See names of variables
names(NLSY_BW)

NLSY IQ.

Description

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.

Usage

NLSY_IQ

Format

A data frame with 6514 observations on 13 variables:

iq_std

standardized IQ score, PIAT score

BF_months

months of breast feeding

mom_drink_preg_all

did mother drink at all during pregnancy

lbw_preterm

low birth weight + preterm

age

age of child

female

child female

black

mother black

motherAge

age of mother

motherEDU

years of schooling of mother

mom_married

is the mother married?

income

annual income of mother

sex

child sex

race

race of mother

Source: https://drive.google.com/file/d/1O1W9dP8F3B1DnAZGBegpoqCfysUrn7Uc/view?usp=sharing

Examples

## Load data set
data("NLSY_IQ")
## See names of variables
names(NLSY_IQ)

Collect parameters from the short, intermediate and auxiliary regressions

Description

Collect parameters from the short, intermediate and auxiliary regressions

Usage

collect_par(data, outcome, treatment, control, other_regressors = NULL)

Arguments

Value

A data frame with the following columns:

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)
   
## 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

Description

Create contour plot of bias

Usage

cplotbias(data)

Arguments

Value

A plot object created with ggplot

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)
   
## 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)

Description

Plot graph of function delta=f(Rmax)

Usage

delfplot(parameters)

Arguments

Value

A plot object created with ggplot

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)
   
## 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

Description

Histogram of bias adjusted treatment effect

Usage

dplotbate(data)

Arguments

Value

A plot object created with ggplot

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)
   
## 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

Description

Extend border of bounded box by +/- e

Usage

expand_border(parameters, deltalow, deltahigh, Rlow, Rhigh, e)

Arguments

Value

Data frame.

Identify all border points in a region

Description

Identify all border points in a region

Usage

get_border(region, e)

Arguments

Value

A data frame containing the x and y coordinates of the border points of the region.

Compute roots of the cubic equation

Description

Compute roots of the cubic equation

Usage

mycubic(parameters, mydelta, Rmax)

Arguments

Value

A vector containing the three roots of the cubic equation defined by the parameters, delta and Rmax.

Evaluates discriminant of the cubic equation

Description

Evaluates discriminant of the cubic equation

Usage

mydisc(parameters, mydelta, Rmax)

Arguments

Value

Returns a value of 0 or 1; 0 (if discriminant is positive) and 1 (if discriminant is nonpositive)

Computes identified set according to Oster (2019)

Description

Computes identified set according to Oster (2019)

Usage

osterbds(parameters, Rmax)

Arguments

Value

A data frame with three columns:

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)
   
## 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)

Description

Computes delta* according to Oster (2019)

Usage

osterdelstar(parameters, Rmax)

Arguments

Value

A data frame with three columns:

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)
   
## 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.

Description

Compute bias adjusted treatment effect taking parameter vector as input.

Usage

ovbias(parameters, deltalow, deltahigh, Rhigh, e)

Arguments

Value

List with three elements:

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)
   
## 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.

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

Value

List with three elements:

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)

Compute bias adjusted treatment effect taking data frame as input.

Description

Compute bias adjusted treatment effect taking data frame as input.

Usage

ovbias_par(
  data,
  outcome,
  treatment,
  control,
  other_regressors = NULL,
  deltalow,
  deltahigh,
  Rhigh,
  e
)

Arguments

Value

List with three elements:

Examples

## 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

Description

Returns coefficients of the cubic equation

Usage

partocoef(parameters, mydelta, Rmax)

Arguments

Value

A data frame with the coefficients of the cubic equation.

Select root of the cubic based on the root of a nearest point

Description

Select root of the cubic based on the root of a nearest point

Usage

selectroot(parameters, mydelta, Rmax, closest_bias)

Arguments

Value

Data frame

Split a region into two parts

Description

Split a region into two parts

Usage

split_nurr(region1, region2, epsilon, parameters, e)

Arguments

Value

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

Description

Region plot to demarcate URR and NURR for the bounded box

Usage

urrplot(parameters, deltalow, deltahigh, Rlow, Rhigh, e)

Arguments

Value

A plot object created by ggplot

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)
   
## 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)