create_model(
formula,
data,
likelihood = "gaussian",
prior = NULL,
c_hat = NULL,
num_trials = NULL,
options = NULL,
prior_only = FALSE,
verbose = FALSE,
sample_f = !(likelihood == "gaussian")
)
Arguments
formula |
The model formula, where
it must contain exatly one tilde (~ ), with response
variable on the left-hand side and model terms on the right-hand side
terms are be separated by a plus (+ ) sign
all variables appearing in formula must be
found in data
See the "Model formula syntax" section below (lgp ) for
instructions on how to specify the model terms. |
data |
A data.frame where each column corresponds to one
variable, and each row is one observation. Continuous covariates and the
response variable must have type "numeric" and categorical covariates
must have type "factor" . Missing values should be indicated with
NaN or NA . The response variable cannot contain missing
values. Column names should not contain trailing or leading underscores. |
likelihood |
Determines the observation model. Must be either
"gaussian" (default), "poisson" , "nb" (negative
binomial), "binomial" or "bb" (beta binomial). |
prior |
A named list, defining the prior distribution of model
(hyper)parameters. See the "Defining priors" section below
(lgp ). |
c_hat |
The GP mean. This should only be given if sample_f is
TRUE , otherwise the GP will always have zero mean. If sample_f
is TRUE , the given c_hat can be a vector of length
dim(data)[1] , or a real number defining a constant GP mean. If not
specified and sample_f is TRUE , c_hat is set to
c_hat = mean(y) , if likelihood is "gaussian" ,
c_hat = log(mean(y)) if likelihood is
"poisson" or "nb" ,
c_hat = log(p/(1-p)) , where
p = mean(y/num_trials) if likelihood is "binomial"
or "bb" ,
where y denotes the response variable measurements. |
num_trials |
This argument (number of trials) is only needed when
likelihood is "binomial" or "bb" . Must have length one or
equal to the number of data points. Setting num_trials=1 and
likelihood="binomial" corresponds to Bernoulli observation model. |
options |
A named list with the following possible fields: If options is NULL , default options are used. The defaults
are equivalent to
options = list(delta = 1e-8, vm_params = c(0.025, 1)) . |
prior_only |
Should likelihood be ignored? See also
sample_param_prior which can be used for any
lgpmodel, and whose runtime is independent of the number of
observations. |
verbose |
Should some informative messages be printed? |
sample_f |
Determines if the latent function values are sampled
(must be TRUE if likelihood is not "gaussian" ). If this is
TRUE , the response variable will be normalized to have zero mean
and unit variance. |
Value
An object of class lgpmodel, containing the
Stan input created based on parsing the specified formula
,
prior
, and other options.
See also