Parse the response variable and its likelihood model
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
NA. The response variable cannot contain missing
values. Column names should not contain trailing or leading underscores.
Determines the observation model. Must be either
"bb" (beta binomial).
The GP mean. This should only be given if
TRUE, otherwise the GP will always have zero mean. If
TRUE, the given
c_hat can be a vector of length
dim(data), or a real number defining a constant GP mean. If not
c_hat is set to
c_hat = mean(y), if
p = mean(y/num_trials) if
y denotes the response variable measurements.
This argument (number of trials) is only needed when
"bb". Must have length one or
equal to the number of data points. Setting
likelihood="binomial" corresponds to Bernoulli observation model.
Name of response variable
Determines if the latent function values are sampled
TRUE if likelihood is not
"gaussian"). If this is
TRUE, the response variable will be normalized to have zero mean
and unit variance.
Should some informative messages be printed?
a list of parsed options