Access the result of a ngme fitted model
ngme_result(ngme_object, model = NULL, transformed = TRUE)
a list of parameters for the specified model, or all models if model is NULL
extract_parameters
for the underlying function
if (FALSE) { # \dontrun{
# Fit a simple AR(1) model
Y <- 1:10; n_obs <- length(Y)
x1 <- runif(n_obs)
x2 <- rexp(n_obs)
ngme_out <- ngme(
Y ~ x1 + x2 + f(
1:n_obs,
name = "my_ar",
model = "ar1",
rho = 0.5,
noise = noise_nig(mu=2, sigma=3, nu=1)
),
data = data.frame(x1=x1, x2=x2, Y=Y)
)
# Get all model parameters (transformed)
all_params <- ngme_result(ngme_out)
# Returns: list(my_ar = list(rho = 0.5, mu = 2, sigma = 3, nu = 1),
# data = list(fixed_effects = c(...), sigma = 0.5))
# Get parameters for specific latent model
ar_params <- ngme_result(ngme_out, model = "my_ar")
# Returns: list(rho = 0.5, mu = 2, sigma = 3, nu = 1)
# Get raw (untransformed) parameters
ar_raw <- ngme_result(ngme_out, model = "my_ar", transformed = FALSE)
# Returns: list(theta_rho = 1.099, mu = 2, sigma = 3, nu = 1)
# Get fixed effects and measurement noise
data_params <- ngme_result(ngme_out, model = "data")
# Returns: list(fixed_effects = c(...), sigma = 0.5, ...)
# For models with multiple latent processes
ngme_out2 <- ngme(
Y ~ x1 + x2 + f(
1:n_obs,
name = "my_ar",
model = "ar1",
noise = noise_nig(mu=2, sigma=3, nu=1)
) + f(
1:n_obs,
name = "my_ou",
model = "ou",
noise = noise_normal(sigma=1)
),
data = data.frame(x1=x1, x2=x2, Y=Y)
)
# Get all models
all_models <- ngme_result(ngme_out2)
# Returns: list(my_ar = list(...), my_ou = list(...), data = list(...))
# Get specific model
ou_params <- ngme_result(ngme_out2, model = "my_ou")
# Returns: list(theta_K1 = 0.5, sigma = 1)
} # }