R/generate_data2.R

In silp: Conditional Process Analysis (CPA) via SEM Approach

# generate_data2 <- function(n_obs, corr, effect, ld, alp ){
#   iv <- mnonr::mnonr(n_obs, 2,5, 15 ,Sigma = matrix(c(1, corr, corr, 1), 2, 2)) #nx2 mnormal variable
#   iv <- cbind(iv, iv[, 1] * iv[, 2]) #a, b, ab, nx3 matrix
# 
#   cov_iv <- matrix(c(1, corr, 0, corr, 1, 0, 0, 0, 1 + corr^2), 3, 3)
#   coef_iv <- matrix(c(.4, .2, effect))
# 
# 
#   #s.t var(y) = 1
#   sd_res <- sqrt(1 - t(coef_iv) %*% cov_iv %*% coef_iv)
# 
#   res <- mnonr::mnonr(n_obs, 1 , 5 ,12, sd_res)
# 
#   dv <- iv %*% coef_iv + res
#   lv <- cbind(dv, iv)
# 
#   #correct latent variable residual variance by alpha
#   error_y <- mnonr::mnonr(n_obs * 4 , 1, 5, 12 , as.matrix(c_res(c(1,1,1,1), alp)))
#   error_x <- mnonr::mnonr(n_obs * 4 , 1, 5, 12 , as.matrix(c_res(c(1,1,1,1), alp)))
#   error_z <- mnonr::mnonr(n_obs * 4 , 1, 5, 12 , as.matrix(c_res(ld, alp)))
# 
# 
#   #y, x, z
#   ov <- lv[,1:3] %*% matrix(c(rep(1,4),rep(0,8),
#                               rep(0,4),rep(1,4),rep(0,4),
#                               rep(0,8),ld ), nrow = 3, byrow = T)
# 
#   ov[,1] = ov[,1] + error_y[1:n_obs]
#   ov[,2] = ov[,2] + error_y[(1*n_obs +1) : (2*n_obs)]
#   ov[,3] = ov[,3] + error_y[(2*n_obs +1) : (3*n_obs)]
#   ov[,4] = ov[,4] + error_y[(3*n_obs +1) : (4*n_obs)]
# 
#   ov[,5] = ov[,5] + error_x[1:n_obs]
#   ov[,6] = ov[,6] + error_x[(1*n_obs +1) : (2*n_obs)]
#   ov[,7] = ov[,7] + error_x[(2*n_obs +1) : (3*n_obs)]
#   ov[,8] = ov[,8] + error_x[(3*n_obs +1) : (4*n_obs)]
# 
#   ov[,9]  = ov[,9]  + error_z[1:n_obs]
#   ov[,10] = ov[,10] + error_z[(1*n_obs +1) : (2*n_obs)]
#   ov[,11] = ov[,11] + error_z[(2*n_obs +1) : (3*n_obs)]
#   ov[,12] = ov[,12] + error_z[(3*n_obs +1) : (4*n_obs)]
# 
#   ov <- data.frame(ov)
# 
#   colnames(ov) <- c("y1", "y2", "y3", "y4",
#                     "x1", "x2", "x3", "x4",
#                     "z1", "z2", "z3", "z4")
#   return(ov)
# }
#