Creates panel or cross-classified data

This function allows the user to create data structures that are paneled or cross-classified: where one level of observation draws simultaneously from two or many source levels. Common examples of panels include country-year data which have country-level and year-level characteristics.

Usage

cross_levels(by = NULL, ...)

link_levels(N = NULL, by = NULL, ...)

Arguments

by

The result of a call to join_using() which specifies how the cross-classified data will be created

...

A variable or series of variables to add to the resulting data frame after the cross-classified data is created.

N

The number of observations in the resulting data frame. If N is NULL or not provided, the join_using will be an "outer product" -- merging each row of each provided data frame with each other data frame to make a full panel.

Value

data.frame

Details

By specifying the appropriate arguments in join_using() within the function call, it is possible to induce correlation in cross-classified data.

Examples

# Generate full panel data
panel <- fabricate(
 countries = add_level(N = 20, country_shock = runif(N, 1, 10)),
 years = add_level(N = 20, year_shock = runif(N, 1, 10), nest=FALSE),
 obs = cross_levels(by = join_using(countries, years), GDP_it = country_shock + year_shock)
)

# Include an "N" argument to allow for cross-classified
# data.
students <- fabricate(
 primary_school = add_level(N = 20, ps_quality = runif(N, 1, 10)),
 secondary_school = add_level(N = 15, ss_quality = runif(N, 1, 10), nest=FALSE),
 students = link_levels(N = 500, by = join_using(primary_school, secondary_school))
)
head(students)
#>   primary_school ps_quality secondary_school ss_quality students
#> 1             15   9.265041               09   6.544130      001
#> 2             06   6.860068               08   9.282482      002
#> 3             11   1.629510               05   8.978955      003
#> 4             09   9.849402               12   7.921122      004
#> 5             07   5.758383               14   5.847698      005
#> 6             07   5.758383               06   6.400336      006

# Induce a correlation structure in cross-classified data by providing
# rho.
students <- fabricate(
 primary_school = add_level(N = 20, ps_quality = runif(N, 1, 10)),
 secondary_school = add_level(N = 15, ss_quality = runif(N, 1, 10), nest=FALSE),
 students = link_levels(N = 500, by = join_using(ps_quality, ss_quality, rho = 0.5))
)
cor(students$ps_quality, students$ss_quality)
#> [1] 0.4551475