hyper_names | R Documentation |

Output all hyperparameter names and help specifying the list of
hyperparameters that is inserted into `robyn_inputs(hyperparameters = ...)`

```
hyper_names(adstock, all_media, all_vars = NULL)
```

`adstock` |
Character. Default to |

`all_media` |
Character vector. Default to |

`all_vars` |
Used to check the penalties inputs, especially for refreshing models. |

Character vector. Names of hyper-parameters that should be defined.

Get correct hyperparameter names: All variables in

`paid_media_vars`

or`organic_vars`

require hyperprameters and will be transformed by adstock & saturation. Difference between`paid_media_vars`

and`organic_vars`

is that`paid_media_vars`

has spend that needs to be specified in`paid_media_spends`

specifically. Run`hyper_names()`

to get correct hyperparameter names. All names in hyperparameters must equal names from`hyper_names()`

, case sensitive.Get guidance for setting hyperparameter bounds: For geometric adstock, use theta, alpha & gamma. For both weibull adstock options, use shape, scale, alpha, gamma.

Theta: In geometric adstock, theta is decay rate. guideline for usual media genre: TV c(0.3, 0.8), OOH/Print/Radio c(0.1, 0.4), digital c(0, 0.3)

Shape: In weibull adstock, shape controls the decay shape. Recommended c(0.0001, 2). The larger, the more S-shape. The smaller, the more L-shape. Channel-type specific values still to be investigated

Scale: In weibull adstock, scale controls the decay inflexion point. Very conservative recommended bounce c(0, 0.1), because scale can increase adstocking half-life greatly. Channel-type specific values still to be investigated

Gamma: In s-curve transformation with hill function, gamma controls the inflexion point. Recommended bounce c(0.3, 1). The larger the gamma, the later the inflection point in the response curve

Set each hyperparameter bounds. They either contains two values e.g. c(0, 0.5), or only one value (in which case you've "fixed" that hyperparameter)

- plot_adstock
Get adstock transformation example plot, helping you understand geometric/theta and weibull/shape/scale transformation

- plot_saturation
Get saturation curve transformation example plot, helping you understand hill/alpha/gamma transformation

```
media <- c("facebook_S", "print_S", "tv_S")
hyper_names(adstock = "geometric", all_media = media)
hyperparameters <- list(
facebook_S_alphas = c(0.5, 3), # example bounds for alpha
facebook_S_gammas = c(0.3, 1), # example bounds for gamma
facebook_S_thetas = c(0, 0.3), # example bounds for theta
print_S_alphas = c(0.5, 3),
print_S_gammas = c(0.3, 1),
print_S_thetas = c(0.1, 0.4),
tv_S_alphas = c(0.5, 3),
tv_S_gammas = c(0.3, 1),
tv_S_thetas = c(0.3, 0.8)
)
# Define hyper_names for weibull adstock
hyper_names(adstock = "weibull", all_media = media)
hyperparameters <- list(
facebook_S_alphas = c(0.5, 3), # example bounds for alpha
facebook_S_gammas = c(0.3, 1), # example bounds for gamma
facebook_S_shapes = c(0.0001, 2), # example bounds for shape
facebook_S_scales = c(0, 0.1), # example bounds for scale
print_S_alphas = c(0.5, 3),
print_S_gammas = c(0.3, 1),
print_S_shapes = c(0.0001, 2),
print_S_scales = c(0, 0.1),
tv_S_alphas = c(0.5, 3),
tv_S_gammas = c(0.3, 1),
tv_S_shapes = c(0.0001, 2),
tv_S_scales = c(0, 0.1)
)
```

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