Nothing
df_sim <- sim_bi_lcsm(
timepoints = 5,
sample.nobs = 500,
na_x_pct = .15,
na_y_pct = .1,
model_x = list(
alpha_constant = TRUE,
beta = TRUE,
phi = FALSE
),
model_x_param = list(
gamma_lx1 = 29,
sigma2_lx1 = .5,
sigma2_ux = .2,
alpha_g2 = -.3,
sigma2_g2 = .6,
sigma_g2lx1 = .2,
beta_x = -.1
),
model_y = list(
alpha_constant = TRUE,
beta = TRUE,
phi = TRUE
),
model_y_param = list(
gamma_ly1 = 15,
sigma2_ly1 = .2,
sigma2_uy = .2,
alpha_j2 = -.4,
sigma2_j2 = .1,
sigma_j2ly1 = .02,
beta_y = -.2,
phi_y = .1
),
coupling = list(xi_lag_yx = TRUE),
coupling_param = list(
sigma_su = .01,
sigma_ly1lx1 = .2,
sigma_g2ly1 = .1,
sigma_j2lx1 = .1,
sigma_j2g2 = .01,
xi_lag_yx = .5
),
seed = 1234
)
test_that("df simulation", {
# Simulate data from bivariate LCSM parameters
# this is the same data as df_sim in the tutorial paper
expect_equal(df_sim, lcsm_data, tolerance = 1)
})
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