make_tfd_dist {deepregression} | R Documentation |

Families for deepregression

```
make_tfd_dist(family, add_const = 1e-08, output_dim = 1L, trafo_list = NULL)
```

`family` |
character vector |

`add_const` |
small positive constant to stabilize calculations |

`output_dim` |
number of output dimensions of the response (larger 1 for multivariate case) |

`trafo_list` |
list of transformations for each distribution parameter. Per default the transformation listed in details is applied. |

To specify a custom distribution, define the a function as follows
```
function(x) do.call(your_tfd_dist, lapply(1:ncol(x)[[1]],
function(i)
your_trafo_list_on_inputs[[i]](
x[,i,drop=FALSE])))
```

and pass it to `deepregression`

via the `dist_fun`

argument.
Currently the following distributions are supported
with parameters (and corresponding inverse link function in brackets):

"normal": normal distribution with location (identity), scale (exp)

"bernoulli": bernoulli distribution with logits (identity)

"bernoulli_prob": bernoulli distribution with probabilities (sigmoid)

"beta": beta with concentration 1 = alpha (exp) and concentration 0 = beta (exp)

"betar": beta with mean (sigmoid) and scale (sigmoid)

"cauchy": location (identity), scale (exp)

"chi2": cauchy with df (exp)

"chi": cauchy with df (exp)

"exponential": exponential with lambda (exp)

"gamma": gamma with concentration (exp) and rate (exp)

"gammar": gamma with location (exp) and scale (exp), following

`gamlss.dist::GA`

, which implies that the expectation is the location, and the variance of the distribution is the`location^2 scale^2`

"gumbel": gumbel with location (identity), scale (exp)

"half_cauchy": half cauchy with location (identity), scale (exp)

"half_normal": half normal with scale (exp)

"horseshoe": horseshoe with scale (exp)

"inverse_gamma": inverse gamma with concentation (exp) and rate (exp)

"inverse_gamma_ls": inverse gamma with location (exp) and variance (1/exp)

"inverse_gaussian": inverse Gaussian with location (exp) and concentation (exp)

"laplace": Laplace with location (identity) and scale (exp)

"log_normal": Log-normal with location (identity) and scale (exp) of underlying normal distribution

"logistic": logistic with location (identity) and scale (exp)

"negbinom": neg. binomial with count (exp) and prob (sigmoid)

"negbinom_ls": neg. binomail with mean (exp) and clutter factor (exp)

"pareto": Pareto with concentration (exp) and scale (1/exp)

"pareto_ls": Pareto location scale version with mean (exp) and scale (exp), which corresponds to a Pareto distribution with parameters scale = mean and concentration = 1/sigma, where sigma is the scale in the pareto_ls version

"poisson": poisson with rate (exp)

"poisson_lograte": poisson with lograte (identity))

"student_t": Student's t with df (exp)

"student_t_ls": Student's t with df (exp), location (identity) and scale (exp)

"uniform": uniform with upper and lower (both identity)

"zinb": Zero-inflated negative binomial with mean (exp), variance (exp) and prob (sigmoid)

"zip": Zero-inflated poisson distribution with mean (exp) and prob (sigmoid)

[Package *deepregression* version 1.0.0 Index]