margex {prediction}

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

See Also

prediction

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")))