list_conditions_datagen: list_conditions_datagen

In tessaleejohnson/corclus: Simulate Data from the Correlated Cluster Multiple Membership Model

list_conditions_datagen

Description

Wraps around list_conditions to expand all combinations of the arguments passed to the function. The arguments can then be passed to purrr::pmap (map2) to iterate over all simulation conditions.

Usage

list_conditions_datagen(
  .n_sch = 50,
  .n_stu = 50,
  .u_resid_var = 0.2,
  .clust_cov = list(c(0.8, 0), c(0.8, 0.2), c(0.8, 0.4)),
  .wt_vec = list(c(0.5, 0.5)),
  .pct_mobile = c(0, 0.25, 0.5),
  .mean_x = 5,
  .var_x = 4,
  .mean_r = 0,
  .var_r = 2,
  .gamma_z = 1,
  .gamma_x = list(c(10, sqrt(17)/2), c(10, sqrt(11/3)/2), c(10, 1/(2 * sqrt(3))))
)

Arguments

.n_sch	Numeric scalar. Gives the total number of schools in the dataset. The variance-covariance matrix for predictor z will have dimensions .n_sch x .n_sch.
.n_stu	A numeric scalar. The number of students attending each school. Note: this is not the total number of students in the dataset, merely the number of students per school.
.u_resid_var	Numeric scalar. Gives the residual variance of u0j (i.e., the variance unexplained after controlling for the school-level predictor, z).
.clust_cov	Numeric vector. The first element of the vector gives the variance of all schools' predictors, z. If present, the second element gives the covariance of z between schools k and k + 1. The values given in .clust_cov apply to all schools (that is, similar to a Toeplitz pattern). Any off-diagonal values (i.e., covariances) not specified will default to 0. The main diagonal is the variance explained by the predictor.
.wt_vec	A numeric vector with length equal to the maximum number of schools attended by students in the data (in this simulation, the maximum number is 2). The values in .wt_vec are used to weight the effects of different schools attended on students. For this study, all mobile students must have the same weights. If different weighting patterns are desired, the code will need to be updated.
.mean_x	Numeric scalar. The mean of the predictor, x.
.var_x	Numeric scalar. The variance of the predictor, x.
.mean_r	Numeric scalar. The mean of the person-level residual, r.
.var_r	Numeric scalar. The variance of the person-level residual, r.
.gamma_z	Numeric scalar. The school-level effect of the z_predictors on the random intercept.
.gamma_x	Numeric vector with length p (where p is the number of model coefficients, including the intercept).

Details

NOTE: All arguments that take objects with length > 1 must wrap those objects in a list. Bad: list_conditions_datagen(.clust_cov = c(0.8, 0)). Good: list_conditions_datagen(.clust_cov = list(c(0.8, 0))).

Value

This function returns a list of all combinations of arguments passed to this function. Each list element is an argument with length equal to the number of combinations.

Examples

## Not run: 

list_conditions_datagen(.nsch = 5, .pct_mobile = 0.1)


## End(Not run)

tessaleejohnson/corclus documentation built on Oct. 11, 2022, 3:46 a.m.