Description Usage Arguments Examples
This function returns the predicted scores based on the current parameter estimates and the data currently entering of this unit.
1 2 | predict(theta = get_theta(), theta_j = get_theta_j(), data_fixed,
data_random)
|
theta |
The current state of model parameters. |
theta_j |
The current state of the individual parameters. |
data_fixed |
The current observation fixed effects variables. |
data_random |
The current observation random effects variables. |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## First we create a dataset, consisting of 2500 observations from 20
## units. The fixed effects have the coefficients 1, 2, 3, 4, and 5. The
## variance of the random effects equals 1, 4, and 9. Lastly the
## residual variance equals 4:
test_data <- build_dataset(n = 1500,
j = 200,
fixed_coef = 1:5,
random_coef_sd = 1:3,
resid_sd = 2)
## fit a multilevel model:
m1 <- sema_fit_df(formula = y ~ 1 + V3 + V4 + V5 + V6 + (1 + V4 + V5 | id),
data_frame = test_data, intercept = TRUE)
## predict for the last row of the dataset:
predict_unit_outcome <- predict(theta = get_theta(model = m1),
theta_j = get_theta_j(model = m1,
id = test_data$id[2000]),
data_fixed = as.numeric(
test_data[2000, 3:7]),
data_random = as.numeric(
test_data[2000, c(3, 5, 6)]))
|
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