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. |

likelihood |
Determines the observation model. Must be either
`"gaussian"` (default), `"poisson"` , `"nb"` (negative
binomial), `"binomial"` or `"bb"` (beta binomial). |

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. |

y_name |
Name of response variable |

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. |

verbose |
Should some informative messages be printed? |

## Value

a list of parsed options

## See also