margex: Artificial data for margins, copied from Stata
In prediction: Tidy, Type-Safe 'prediction()' Methods

margex

R Documentation

Artificial data for margins, copied from Stata

Description

The dataset is identical to the one provided by Stata and available from webuse::webuse("margex") with categorical variables explicitly encoded as factors.

Usage

data("margex")

Format

A data frame with 3000 observations on the following 11 variables.

‘⁠y⁠’: A numeric vector
‘⁠outcome⁠’: A binary numeric vector with values (0,1)
‘⁠sex⁠’: A factor with two levels
‘⁠group⁠’: A factor with three levels
‘⁠age⁠’: A numeric vector
‘⁠distance⁠’: A numeric vector
‘⁠ycn⁠’: A numeric vector
‘⁠yc⁠’: A numeric vector
‘⁠treatment⁠’: A factor with two levels
‘⁠agegroup⁠’: A factor with three levels
‘⁠arm⁠’: A factor with three levels

Source

https://www.stata-press.com/data/r14/margex.dta

Examples



# Examples from Stata's help files
# Also available from: webuse::webuse("margex")
data("margex")

# A simple case after regress
# . regress y i.sex i.group
# . margins sex
m1 <- lm(y ~ factor(sex) + factor(group), data = margex)
prediction(m1, at = list(sex = c("male", "female")))

# A simple case after logistic
# . logistic outcome i.sex i.group
# . margins sex
m2 <- glm(outcome ~ sex + group, binomial(), data = margex)
prediction(m2, at = list(sex = c("male", "female")))

# Average response versus response at average
# . margins sex
prediction(m2, at = list(sex = c("male", "female")))
# . margins sex, atmeans
## TODO

# Multiple margins from one margins command
# . margins sex group
prediction(m2, at = list(sex = c("male", "female")))
prediction(m2, at = list(group = c("1", "2", "3")))

# Margins with interaction terms
# . logistic outcome i.sex i.group sex#group
# . margins sex group
m3 <- glm(outcome ~ sex * group, binomial(), data = margex)
prediction(m3, at = list(sex = c("male", "female")))
prediction(m3, at = list(group = c("1", "2", "3")))

# Margins with continuous variables
# . logistic outcome i.sex i.group sex#group age
# . margins sex group
m4 <- glm(outcome ~ sex * group + age, binomial(), data = margex)
prediction(m4, at = list(sex = c("male", "female")))
prediction(m4, at = list(group = c("1", "2", "3")))

# Margins of continuous variables
# . margins, at(age=40)
prediction(m4, at = list(age = 40))
# . margins, at(age=(30 35 40 45 50))
prediction(m4, at = list(age = c(30, 35, 40, 45, 50)))

# Margins of interactions
# . margins sex#group
prediction(m4, at = list(sex = c("male", "female"), group = c("1", "2", "3")))

prediction documentation built on June 22, 2024, 10:18 a.m.