Parse the response variable and its likelihood model

create_model.likelihood(
data,
likelihood,
c_hat,
num_trials,
y_name,
sample_f,
verbose
)

## Arguments

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. Determines the observation model. Must be either "gaussian" (default), "poisson", "nb" (negative binomial), "binomial" or "bb" (beta binomial). 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. 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. Name of response variable 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. Should some informative messages be printed?

## Value

a list of parsed options

Other internal model creation functions: create_model.covs_and_comps(), create_model.formula(), create_model.prior()