# Helper function handling specification of which variables to join a cross-classified data on, and what kind of correlation structure needed. Correlation structures can only be provided if the underlying call is a `link_levels()`

call.

Source: `R/cross_levels.R`

`join_using.Rd`

Helper function handling specification of which variables to join a
cross-classified data on, and what kind of correlation structure needed.
Correlation structures can only be provided if the underlying call is
a `link_levels()`

call.

## Arguments

- ...
A series of two or more variable names, unquoted, to join on in order to create cross-classified data.

- rho
A fixed (Spearman's rank) correlation coefficient between the variables being joined on: note that if it is not possible to make a correlation matrix from this coefficient (e.g. if you are joining on three or more variables and rho is negative) then the

`cross_levels()`

call will fail. Do not provide`rho`

if making panel data.- sigma
A matrix with dimensions equal to the number of variables you are joining on, specifying the correlation for the resulting joined data. Only one of rho and sigma should be provided. Do not provide

`sigma`

if making panel data.

## Examples

```
panels <- fabricate(
countries = add_level(N = 150, country_fe = runif(N, 1, 10)),
years = add_level(N = 25, year_shock = runif(N, 1, 10), nest = FALSE),
obs = cross_levels(
by = join_using(countries, years),
new_variable = country_fe + year_shock + rnorm(N, 0, 2)
)
)
schools_data <- fabricate(
primary_schools = add_level(N = 20, ps_quality = runif(N, 1, 10)),
secondary_schools = add_level(
N = 15,
ss_quality = runif(N, 1, 10),
nest = FALSE),
students = link_levels(
N = 1500,
by = join_using(primary_schools, secondary_schools),
SAT_score = 800 + 13 * ps_quality + 26 * ss_quality + rnorm(N, 0, 50)
)
)
```