corclus_params: corclus_params

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

corclus_params

Description

This documentation includes parameters for most functions in the corclus package.

Usage

corclus_params()

Arguments

.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.
.gamma_x	Numeric vector with length p (where p is the number of model coefficients, including the intercept).
.gamma_z	Numeric scalar. The school-level effect of the z_predictors on the random intercept.
.id_nonmob	Logical. Indicates whether non-mobile students should receive a non-zero school ID for their second school. Technically, it shouldn't matter if non-mobile students have a non-zero school ID (i.e., if first and second schools are the same), so all ID schemes should be equivalent, but it may matter for passing data to MLwiN for estimation. See runMLwiN for more information.
.mean_r	Numeric scalar. The mean of the person-level residual, r.
.mean_x	Numeric scalar. The mean of the predictor, x.
.mm_id_nms	A string. The prefix name of the multiple membership unique ID variables.
.mm_wt_nms	A string. The prefix name of the multiple membership weight variables.
.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.
.per_resid	Numeric vector with length n (where n is the number of persons in the data). Gives the person-level residual for the model.
.sch_dat	A matrix or dataframe. The school-level information created by the gen_u_mmrem function.
.sch_exp	A matrix or dataframe. The school-level information created by the gen_u_mmrem function and expanded by the expand_sch_info function.
.sch_predictor	Numeric matrix or dataframe. Contains the values of the school-level predictor, z, generated in gen_u_mmrem.
.sch_resid	Numeric matrix with dimensions n x h (where n is the number of persons and h is the maximum number of schools attended by any person in the dataset). The hth column of the matrix should give the residual for the hth school attended by person i. As mentioned above, all students were initially assigned a mobility profile that included multiple schools, then only a certain proportion of those mobility profiles were retained.
.sch_weight	Numeric matrix with dimensions n x h (where n is the number of persons and h is the maximum number of schools attended by any person in the dataset). Rows should sum to 1 (that is, for each student, the weights assigned to their schools attended should sum to 1). For a school a student did not attend, the weight should be 0 (that is, if the maximum number of schools attended was 2 and person A only attended 1 school, then the weight for their "second school" should be 0, while the weight for their "first school" should be 1). To simulate the data, all students were initially assigned a mobility profile (meaning that all students were assigned h schools to attend), and then only a certain proportion of students were coded as mobile. For the students who were coded as mobile, their .sch_weight matrix row should give equal weight to all schools attended. For students who were coded as non-mobile, their first school was given a weight of 1 and all other schools were given weights of 0.
.strings	A character vector. Gives the strings to be extracted by .capture_groups.
.u_resid_var	Numeric scalar. Gives the residual variance of u0j (i.e., the variance unexplained after controlling for the school-level predictor, z).
.var_r	Numeric scalar. The variance of the person-level residual, r.
.var_x	Numeric scalar. The variance of the predictor, x.
.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.
.wt_nonmob	Logical. Indicates whether non-mobile students should receive the same weights as mobile students. Technically, it shouldn't matter if non-mobile students are given the same weights because their first and second schools are the same, so all weighting schemes should be equivalent, but it may matter for passing data to MLwiN for estimation. See runMLwiN for more information.
.x_predictor	Numeric matrix with dimensions n x p (where n is the num of persons and p is the number of coefficients, including the intercept). The column of .design_x corresponding to the intercept should be a column of 1s.

Details

Arguments for "self-contained" functions like is_off_diag, pivot_longer_multicol, pivot_wider_multicol, plan_future, and simulate_mobility are not included in this documentation.

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