Package 'Robyn' reference manual

Title:	Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science
Description:	Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.
Authors:	Gufeng Zhou [aut], Bernardo Lares [cre, aut], Leonel Sentana [aut], Igor Skokan [aut], Meta Platforms, Inc. [cph, fnd]
Maintainer:	Bernardo Lares <[email protected]>
License:	MIT + file LICENSE
Version:	3.11.1
Built:	2024-09-26 06:24:16 UTC
Source:	CRAN

Adstocking Transformation (Geometric and Weibull)

Description

adstock_geometric() for Geometric Adstocking is the classic one-parametric adstock function.

adstock_weibull() for Weibull Adstocking is a two-parametric adstock function that allows changing decay rate over time, as opposed to the fixed decay rate over time as in Geometric adstock. It has two options, the cumulative density function "CDF" or the probability density function "PDF".

Usage

adstock_geometric(x, theta)

adstock_weibull(x, shape, scale, windlen = length(x), type = "cdf")

transform_adstock(
  x,
  adstock,
  theta = NULL,
  shape = NULL,
  scale = NULL,
  windlen = length(x)
)

plot_adstock(plot = TRUE)
adstock_geometric(x, theta)

adstock_weibull(x, shape, scale, windlen = length(x), type = "cdf")

transform_adstock(
  x,
  adstock,
  theta = NULL,
  shape = NULL,
  scale = NULL,
  windlen = length(x)
)

plot_adstock(plot = TRUE)

Arguments

`x`	A numeric vector.
`theta`	Numeric. Theta is the only parameter on Geometric Adstocking and means fixed decay rate. Assuming TV spend on day 1 is 100€ and theta = 0.7, then day 2 has 100 x 0.7 = 70€ worth of effect carried-over from day 1, day 3 has 70 x 0.7 = 49€ from day 2 etc. Rule-of-thumb for common media genre: TV c(0.3, 0.8), OOH/Print/ Radio c(0.1, 0.4), digital c(0, 0.3).
`shape`, `scale`	Numeric. Check "Details" section for more details.
`windlen`	Integer. Length of modelling window. By default, same length as `x`.
`type`	Character. Accepts "CDF" or "PDF". CDF, or cumulative density function of the Weibull function allows changing decay rate over time in both C and S shape, while the peak value will always stay at the first period, meaning no lagged effect. PDF, or the probability density function, enables peak value occurring after the first period when shape >=1, allowing lagged effect.
`adstock`	Character. One of: "geometric", "weibull_cdf", "weibull_pdf".
`plot`	Boolean. Do you wish to return the plot?

Details

Weibull's CDF (Cumulative Distribution Function): has two parameters, shape & scale, and has flexible decay rate, compared to Geometric adstock with fixed decay rate. The shape parameter controls the shape of the decay curve. Recommended bound is c(0.0001, 2). The larger the shape, the more S-shape. The smaller, the more L-shape. Scale controls the inflexion point of the decay curve. We recommend very conservative bounce of c(0, 0.1), because scale increases the adstock half-life greatly.
Weibull's PDF (Probability Density Function): also shape & scale as parameter and also has flexible decay rate as Weibull CDF. The difference is that Weibull PDF offers lagged effect. When shape > 2, the curve peaks after x = 0 and has NULL slope at x = 0, enabling lagged effect and sharper increase and decrease of adstock, while the scale parameter indicates the limit of the relative position of the peak at x axis; when 1 < shape < 2, the curve peaks after x = 0 and has infinite positive slope at x = 0, enabling lagged effect and slower increase and decrease of adstock, while scale has the same effect as above; when shape = 1, the curve peaks at x = 0 and reduces to exponential decay, while scale controls the inflexion point; when 0 < shape < 1, the curve peaks at x = 0 and has increasing decay, while scale controls the inflexion point. When all possible shapes are relevant, we recommend c(0.0001, 10) as bounds for shape; when only strong lagged effect is of interest, we recommend c(2.0001, 10) as bound for shape. In all cases, we recommend conservative bound of c(0, 0.1) for scale. Due to the great flexibility of Weibull PDF, meaning more freedom in hyperparameter spaces for Nevergrad to explore, it also requires larger iterations to converge.

Run plot_adstock() to see the difference visually.

Value

Numeric values. Transformed values.

Examples

adstock_geometric(rep(100, 5), theta = 0.5)
adstock_weibull(rep(100, 5), shape = 0.5, scale = 0.5, type = "CDF")
adstock_weibull(rep(100, 5), shape = 0.5, scale = 0.5, type = "PDF")

# Wrapped function for either adstock
transform_adstock(rep(100, 10), "weibull_pdf", shape = 1, scale = 0.5)
adstock_geometric(rep(100, 5), theta = 0.5)
adstock_weibull(rep(100, 5), shape = 0.5, scale = 0.5, type = "CDF")
adstock_weibull(rep(100, 5), shape = 0.5, scale = 0.5, type = "PDF")

# Wrapped function for either adstock
transform_adstock(rep(100, 10), "weibull_pdf", shape = 1, scale = 0.5)

Robyn Dataset: Holidays by Country

Description

Contains prophet's "new" default holidays by country. When using own holidays, please keep the header c("ds", "holiday", "country", "year").

Usage

data(dt_prophet_holidays)
data(dt_prophet_holidays)

Format

An object of class "data.frame"

ds: Date
holiday: Name of celebrated holiday
country: Code for the country (Alpha-2)
year: Year of ds

Value

data.frame

Dataframe. Contains prophet's default holidays by country.

Examples

data(dt_prophet_holidays)
head(dt_prophet_holidays)
data(dt_prophet_holidays)
head(dt_prophet_holidays)

Robyn Dataset: MMM Demo Data

Description

Simulated MMM data. Input time series should be daily, weekly or monthly.

Usage

data(dt_simulated_weekly)
data(dt_simulated_weekly)

Format

An object of class "data.frame"

DATE: Date
revenue: Daily total revenue
tv_S: Television
ooh_S: Out of home
...: ...

Value

data.frame

Dataframe. Contains simulated dummy dataset to test and run demo.

Examples

data(dt_simulated_weekly)
head(dt_simulated_weekly)
data(dt_simulated_weekly)
head(dt_simulated_weekly)

Fit a nonlinear model for media spend and exposure

Description

This function is called in robyn_engineering(). It uses the Michaelis-Menten function to fit the nonlinear model. Fallback model is the simple linear model lm() in case the nonlinear model is fitting worse. A bad fit here might result in unreasonable model results. Two options are recommended: Either splitting the channel into sub-channels to achieve better fit, or just use spend as paid_media_vars

Usage

fit_spend_exposure(dt_spendModInput, mediaCostFactor, paid_media_var)
fit_spend_exposure(dt_spendModInput, mediaCostFactor, paid_media_var)

Arguments

`dt_spendModInput`	data.frame. Containing channel spends and exposure data.
`mediaCostFactor`	Numeric vector. The ratio between raw media exposure and spend metrics.
`paid_media_var`	Character. Paid media variable.

Value

List. Containing the all spend-exposure model results.

Check hyperparameter limits

Description

Reference data.frame that shows the upper and lower bounds valid for each hyperparameter.

Usage

hyper_limits()
hyper_limits()

Value

Dataframe. Contains upper and lower bounds for each hyperparameter.

Examples

hyper_limits()
hyper_limits()

Get correct hyperparameter names

Description

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

Usage

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

Arguments

`adstock`	Character. Default to `InputCollect$adstock`. Accepts "geometric", "weibull_cdf" or "weibull_pdf"
`all_media`	Character vector. Default to `InputCollect$all_media`. Includes `InputCollect$paid_media_spends` and `InputCollect$organic_vars`.
`all_vars`	Used to check the penalties inputs, especially for refreshing models.

Value

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

Guide to setup hyperparameters

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)

Helper plots

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

Examples


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)
)

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)
)

Michaelis-Menten Transformation

Description

The Michaelis-Menten mic_men() function is used to fit the spend exposure relationship for paid media variables, when exposure metrics like impressions, clicks or GRPs are provided in paid_media_vars instead of spend metric.

Usage

mic_men(x, Vmax, Km, reverse = FALSE)
mic_men(x, Vmax, Km, reverse = FALSE)

Arguments

`x`	Numeric value or vector. Input media spend when `reverse = FALSE`. Input media exposure metrics (impression, clicks, GRPs, etc.) when `reverse = TRUE`.
`Vmax`	Numeric Indicates maximum rate achieved by the system.
`Km`	Numeric. The Michaelis constant.
`reverse`	Boolean. Input media spend when `reverse = FALSE`. Input media exposure metrics (impression, clicks, GRPs etc.) when `reverse = TRUE`.

Value

Numeric values. Transformed values.

Examples

mic_men(x = 5:10, Vmax = 5, Km = 0.5)
mic_men(x = 5:10, Vmax = 5, Km = 0.5)

Conduct prophet decomposition

Description

When prophet_vars in robyn_inputs() is specified, this function decomposes trend, season, holiday and weekday from the dependent variable.

Usage

prophet_decomp(
  dt_transform,
  dt_holidays,
  prophet_country,
  prophet_vars,
  prophet_signs,
  factor_vars,
  context_vars,
  organic_vars,
  paid_media_spends,
  intervalType,
  dayInterval,
  custom_params
)
prophet_decomp(
  dt_transform,
  dt_holidays,
  prophet_country,
  prophet_vars,
  prophet_signs,
  factor_vars,
  context_vars,
  organic_vars,
  paid_media_spends,
  intervalType,
  dayInterval,
  custom_params
)

Arguments

`dt_transform`	A data.frame with all model features. Must contain `ds` column for time variable values and `dep_var` column for dependent variable values.
`dt_holidays`	data.frame. Raw input holiday data. Load standard Prophet holidays using `data("dt_prophet_holidays")`
`context_vars`, `paid_media_spends`, `intervalType`, `dayInterval`, `prophet_country`, `prophet_vars`, `prophet_signs`, `factor_vars`	As included in `InputCollect`
`organic_vars`	Character vector. Typically newsletter sendings, push-notifications, social media posts etc. Compared to `paid_media_vars` `organic_vars` are often marketing activities without clear spends.
`custom_params`	List. Custom parameters passed to `prophet()`

Value

A list containing all prophet decomposition output.

Robyn MMM Project from Meta Marketing Science

Description

Robyn is an automated Marketing Mix Modeling (MMM) code. It aims to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocator and diminishing returns curves and allows ground-truth calibration to account for causation.

Author(s)

Gufeng Zhou ([email protected])

Leonel Sentana ([email protected])

Igor Skokan ([email protected])

Bernardo Lares ([email protected])

Budget Allocator

Description

robyn_allocator() function returns a new split of media variable spends that maximizes the total media response.

Usage

robyn_allocator(
  robyn_object = NULL,
  select_build = 0,
  InputCollect = NULL,
  OutputCollect = NULL,
  select_model = NULL,
  json_file = NULL,
  scenario = "max_response",
  total_budget = NULL,
  target_value = NULL,
  date_range = "all",
  channel_constr_low = NULL,
  channel_constr_up = NULL,
  channel_constr_multiplier = 3,
  optim_algo = "SLSQP_AUGLAG",
  maxeval = 1e+05,
  constr_mode = "eq",
  plots = TRUE,
  plot_folder = NULL,
  plot_folder_sub = NULL,
  export = TRUE,
  quiet = FALSE,
  ui = FALSE,
  ...
)

## S3 method for class 'robyn_allocator'
print(x, ...)

## S3 method for class 'robyn_allocator'
plot(x, ...)
robyn_allocator(
  robyn_object = NULL,
  select_build = 0,
  InputCollect = NULL,
  OutputCollect = NULL,
  select_model = NULL,
  json_file = NULL,
  scenario = "max_response",
  total_budget = NULL,
  target_value = NULL,
  date_range = "all",
  channel_constr_low = NULL,
  channel_constr_up = NULL,
  channel_constr_multiplier = 3,
  optim_algo = "SLSQP_AUGLAG",
  maxeval = 1e+05,
  constr_mode = "eq",
  plots = TRUE,
  plot_folder = NULL,
  plot_folder_sub = NULL,
  export = TRUE,
  quiet = FALSE,
  ui = FALSE,
  ...
)

## S3 method for class 'robyn_allocator'
print(x, ...)

## S3 method for class 'robyn_allocator'
plot(x, ...)

Arguments

`robyn_object`	Character or List. Path of the `Robyn.RDS` object that contains all previous modeling information or the imported list.
`select_build`	Integer. Default to the latest model build. `select_build = 0` selects the initial model. `select_build = 1` selects the first refresh model.
`InputCollect`	List. Contains all input parameters for the model. Required when `robyn_object` is not provided.
`OutputCollect`	List. Containing all model result. Required when `robyn_object` is not provided.
`select_model`	Character. A model `SolID`. When `robyn_object` is provided, `select_model` defaults to the already selected `SolID`. When `robyn_object` is not provided, `select_model` must be provided with `InputCollect` and `OutputCollect`, and must be one of `OutputCollect$allSolutions`.
`json_file`	Character. JSON file to import previously exported inputs or recreate a model. To generate this file, use `robyn_write()`. If you didn't export your data in the json file as "raw_data", `dt_input` must be provided; `dt_holidays` input is optional.
`scenario`	Character. Accepted options are: `"max_response"`, `"target_efficiency"`. Scenario `"max_response"` answers the question: "What's the potential revenue/conversions lift with the same (or custom) spend level in `date_range` and what is the allocation and expected response mix?" Scenario `"target_efficiency"` optimizes ROAS or CPA and answers the question: "What's the potential revenue/conversions lift and spend levels based on a `target_value` for CPA/ROAS and what is the allocation and expected response mix?" Deprecated scenario: `"max_response_expected_spend"`.
`total_budget`	Numeric. Total marketing budget for all paid channels for the period in `date_range`.
`target_value`	Numeric. When using the scenario `"target_efficiency"`, target_value is the desired ROAS or CPA with no upper spend limit. Default is set to 80% of initial ROAS or 120% of initial CPA, when `"target_value = NULL"`.
`date_range`	Character. Date(s) to apply adstocked transformations and pick mean spends per channel. Set one of: "all", "last", or "last_n" (where n is the last N dates available), date (i.e. "2022-03-27"), or date range (i.e. `c("2022-01-01", "2022-12-31")`). Default to "all".
`channel_constr_low`, `channel_constr_up`	Numeric vectors. The lower and upper bounds for each paid media variable when maximizing total media response. For example, `channel_constr_low = 0.7` means minimum spend of the variable is 70 average, using non-zero spend values, within `date_min` and `date_max` date range. Both constrains must be length 1 (same for all values) OR same length and order as `paid_media_spends`. It's not recommended to 'exaggerate' upper bounds, especially if the new level is way higher than historical level. Lower bound must be >=0.01, and upper bound should be < 5.
`channel_constr_multiplier`	Numeric. Default to 3. For example, if `channel_constr_low` and `channel_constr_up` are 0.8 to 1.2, the range is 0.4. The allocator will also show the optimum solution for a larger constraint range of 0.4 x 3 = 1.2, or 0.4 to 1.6, to show the optimization potential to support allocation interpretation and decision.
`optim_algo`	Character. Default to `"SLSQP_AUGLAG"`, short for "Sequential Least-Squares Quadratic Programming" and "Augmented Lagrangian". Alternatively, "`"MMA_AUGLAG"`, short for "Methods of Moving Asymptotes". More details see the documentation of NLopt here.
`maxeval`	Integer. The maximum iteration of the global optimization algorithm. Defaults to 100000.
`constr_mode`	Character. Options are `"eq"` or `"ineq"`, indicating constraints with equality or inequality.
`plots`	Boolean. Generate plots?
`plot_folder`	Character. Path for saving plots and files. Default to `robyn_object` and saves plot in the same directory as `robyn_object`.
`plot_folder_sub`	Character. Sub path for saving plots. Will overwrite the default path with timestamp or, for refresh and allocator, simply overwrite files.
`export`	Boolean. Export outcomes into local files?
`quiet`	Boolean. Keep messages off?
`ui`	Boolean. Save additional outputs for UI usage. List outcome.
`...`	Additional parameters passed to `robyn_outputs()`.
`x`	`robyn_allocator()` output.

Value

A list object containing allocator result.

List. Contains optimized allocation results and plots.

Examples

## Not run: 
# Having InputCollect and OutputCollect results
AllocatorCollect <- robyn_allocator(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = "1_2_3",
  scenario = "max_response",
  channel_constr_low = 0.7,
  channel_constr_up = c(1.2, 1.5, 1.5, 1.5, 1.5),
  channel_constr_multiplier = 4,
  date_range = "last_26",
  export = FALSE
)
# Print a summary
print(AllocatorCollect)
# Plot the allocator one-pager
plot(AllocatorCollect)

## End(Not run)
## Not run: 
# Having InputCollect and OutputCollect results
AllocatorCollect <- robyn_allocator(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = "1_2_3",
  scenario = "max_response",
  channel_constr_low = 0.7,
  channel_constr_up = c(1.2, 1.5, 1.5, 1.5, 1.5),
  channel_constr_multiplier = 4,
  date_range = "last_26",
  export = FALSE
)
# Print a summary
print(AllocatorCollect)
# Plot the allocator one-pager
plot(AllocatorCollect)

## End(Not run)

Clustering to Reduce Number of Models based on ROI and Errors

Description

robyn_clusters() uses output from robyn_run(), to reduce the number of models and create bootstrapped confidence interval and help the user pick up the best (lowest combined error) of the most different kinds (clusters) of models.

Usage

robyn_clusters(
  input,
  dep_var_type,
  cluster_by = "hyperparameters",
  all_media = NULL,
  k = "auto",
  wss_var = 0.06,
  max_clusters = 10,
  limit = 1,
  weights = rep(1, 3),
  dim_red = "PCA",
  quiet = FALSE,
  export = FALSE,
  seed = 123,
  ...
)
robyn_clusters(
  input,
  dep_var_type,
  cluster_by = "hyperparameters",
  all_media = NULL,
  k = "auto",
  wss_var = 0.06,
  max_clusters = 10,
  limit = 1,
  weights = rep(1, 3),
  dim_red = "PCA",
  quiet = FALSE,
  export = FALSE,
  seed = 123,
  ...
)

Arguments

`input`	`robyn_export()`'s output or `pareto_aggregated.csv` results.
`dep_var_type`	Character. For dep_var_type 'revenue', ROI is used for clustering. For conversion', CPA is used for clustering.
`cluster_by`	Character. Any of: "performance" or "hyperparameters".
`all_media`	Character vector. Default to `InputCollect$all_media`. Includes `InputCollect$paid_media_spends` and `InputCollect$organic_vars`.
`k`	Integer. Number of clusters
`wss_var`	Numeric. Used to pick automatic `k` value, when `k` is `NULL` based on WSS variance while considering `limit` clusters. Values between (0, 1). Default value could be 0.05 to consider convergence.
`max_clusters`	Integer. Maximum number of clusters.
`limit`	Integer. Top N results per cluster. If kept in "auto", will select k as the cluster in which the WSS variance was less than 5%.
`weights`	Vector, size 3. How much should each error weight? Order: nrmse, decomp.rssd, mape. The highest the value, the closer it will be scaled to origin. Each value will be normalized so they all sum 1.
`dim_red`	Character. Select dimensionality reduction technique. Pass any of: `c("PCA", "tSNE", "all", "none")`.
`quiet`	Boolean. Keep quiet? If not, print messages.
`export`	Export plots into local files?
`seed`	Numeric. Seed for reproducibility
`...`	Additional parameters passed to `lares::clusterKmeans()`.

Value

List. Clustering results as labeled data.frames and plots.

Author(s)

Bernardo Lares ([email protected])

Examples

## Not run: 
# Having InputCollect and OutputCollect results
cls <- robyn_clusters(
  input = OutputCollect,
  all_media = InputCollect$all_media,
  k = 3, limit = 2,
  weights = c(1, 1, 1.5)
)

## End(Not run)
## Not run: 
# Having InputCollect and OutputCollect results
cls <- robyn_clusters(
  input = OutputCollect,
  all_media = InputCollect$all_media,
  k = 3, limit = 2,
  weights = c(1, 1, 1.5)
)

## End(Not run)

Check Models Convergence

Description

robyn_converge() consumes robyn_run() outputs and calculate convergence status and builds convergence plots. Convergence is calculated by default using the following criteria (having kept the default parameters: sd_qtref = 3 and med_lowb = 2):

Criteria #1:: Last quantile's standard deviation < first 3 quantiles' mean standard deviation
Criteria #2:: Last quantile's absolute median < absolute first quantile's absolute median - 2 * first 3 quantiles' mean standard deviation

Both mentioned criteria have to be satisfied to consider MOO convergence.

Usage

robyn_converge(
  OutputModels,
  n_cuts = 20,
  sd_qtref = 3,
  med_lowb = 2,
  nrmse_win = c(0, 0.998),
  ...
)
robyn_converge(
  OutputModels,
  n_cuts = 20,
  sd_qtref = 3,
  med_lowb = 2,
  nrmse_win = c(0, 0.998),
  ...
)

Arguments

`OutputModels`	List. Output from `robyn_run()`.
`n_cuts`	Integer. Default to 20 (5% cuts each).
`sd_qtref`	Integer. Reference quantile of the error convergence rule for standard deviation (Criteria #1). Defaults to 3.
`med_lowb`	Integer. Lower bound distance of the error convergence rule for median. (Criteria #2). Default to 3.
`nrmse_win`	Numeric vector. Lower and upper quantiles thresholds to winsorize NRMSE. Set values within [0,1]; default: c(0, 0.998) which is 1/500.
`...`	Additional parameters

Value

List. Plots and MOO convergence results.

Examples

## Not run: 
# Having OutputModels results
MOO <- robyn_converge(
  OutputModels,
  n_cuts = 10,
  sd_qtref = 3,
  med_lowb = 3
)

## End(Not run)
## Not run: 
# Having OutputModels results
MOO <- robyn_converge(
  OutputModels,
  n_cuts = 10,
  sd_qtref = 3,
  med_lowb = 3
)

## End(Not run)

Input Data Check & Transformation

Description

robyn_inputs() is the function to input all model parameters and check input correctness for the initial model build. It includes the engineering process results that conducts trend, season, holiday & weekday decomposition using Facebook's time-series forecasting library prophet and fit a nonlinear model to spend and exposure metrics in case exposure metrics are used in paid_media_vars.

Usage

robyn_inputs(
  dt_input = NULL,
  dep_var = NULL,
  dep_var_type = NULL,
  date_var = "auto",
  paid_media_spends = NULL,
  paid_media_vars = NULL,
  paid_media_signs = NULL,
  organic_vars = NULL,
  organic_signs = NULL,
  context_vars = NULL,
  context_signs = NULL,
  factor_vars = NULL,
  dt_holidays = Robyn::dt_prophet_holidays,
  prophet_vars = NULL,
  prophet_signs = NULL,
  prophet_country = NULL,
  adstock = NULL,
  hyperparameters = NULL,
  window_start = NULL,
  window_end = NULL,
  calibration_input = NULL,
  json_file = NULL,
  InputCollect = NULL,
  ...
)

## S3 method for class 'robyn_inputs'
print(x, ...)
robyn_inputs(
  dt_input = NULL,
  dep_var = NULL,
  dep_var_type = NULL,
  date_var = "auto",
  paid_media_spends = NULL,
  paid_media_vars = NULL,
  paid_media_signs = NULL,
  organic_vars = NULL,
  organic_signs = NULL,
  context_vars = NULL,
  context_signs = NULL,
  factor_vars = NULL,
  dt_holidays = Robyn::dt_prophet_holidays,
  prophet_vars = NULL,
  prophet_signs = NULL,
  prophet_country = NULL,
  adstock = NULL,
  hyperparameters = NULL,
  window_start = NULL,
  window_end = NULL,
  calibration_input = NULL,
  json_file = NULL,
  InputCollect = NULL,
  ...
)

## S3 method for class 'robyn_inputs'
print(x, ...)

Arguments

`dt_input`	data.frame. Raw input data. Load simulated dataset using `data("dt_simulated_weekly")`
`dep_var`	Character. Name of dependent variable. Only one allowed
`dep_var_type`	Character. Type of dependent variable as "revenue" or "conversion". Will be used to calculate ROI or CPI, respectively. Only one allowed and case sensitive.
`date_var`	Character. Name of date variable. Daily, weekly and monthly data supported. `date_var` must have format "2020-01-01" (YYY-MM-DD). Default to automatic date detection.
`paid_media_spends`	Character vector. Names of the paid media variables. The values on each of these variables must be numeric. Also, `paid_media_spends` must have same order and length as `paid_media_vars` respectively.
`paid_media_vars`	Character vector. Names of the paid media variables' exposure level metrics (impressions, clicks, GRP etc) other than spend. The values on each of these variables must be numeric. These variables are not being used to train the model but to check relationship and recommend to split media channels into sub-channels (e.g. fb_retargeting, fb_prospecting, etc.) to gain more variance. `paid_media_vars` must have same order and length as `paid_media_spends` respectively and is not required.
`paid_media_signs`	Character vector. Choose any of `c("default", "positive", "negative")`. Control the signs of coefficients for `paid_media_vars`. Must have same order and same length as `paid_media_vars`. By default, all values are set to 'positive'.
`organic_vars`	Character vector. Typically newsletter sendings, push-notifications, social media posts etc. Compared to `paid_media_vars` `organic_vars` are often marketing activities without clear spends.
`organic_signs`	Character vector. Choose any of "default", "positive", "negative". Control the signs of coefficients for `organic_vars` Must have same order and same length as `organic_vars`. By default, all values are set to "positive".
`context_vars`	Character vector. Typically competitors, price & promotion, temperature, unemployment rate, etc.
`context_signs`	Character vector. Choose any of `c("default", "positive", "negative")`. Control the signs of coefficients for context_vars. Must have same order and same length as `context_vars`. By default it's set to 'defualt'.
`factor_vars`	Character vector. Specify which of the provided variables in organic_vars or context_vars should be forced as a factor.
`dt_holidays`	data.frame. Raw input holiday data. Load standard Prophet holidays using `data("dt_prophet_holidays")`
`prophet_vars`	Character vector. Include any of "trend", "season", "weekday", "monthly", "holiday" or NULL. Highly recommended to use all for daily data and "trend", "season", "holiday" for weekly and above cadence. Set to NULL to skip prophet's functionality.
`prophet_signs`	Character vector. Choose any of "default", "positive", "negative". Control the signs of coefficients for `prophet_vars`. Must have same order and same length as `prophet_vars`. By default, all values are set to "default".
`prophet_country`	Character. Only one country allowed. Includes national holidays for all countries, whose list can be found loading `data("dt_prophet_holidays")`.
`adstock`	Character. Choose any of "geometric", "weibull_cdf", "weibull_pdf". Weibull adstock is a two-parametric function and thus more flexible, but takes longer time than the traditional geometric one-parametric function. CDF, or cumulative density function of the Weibull function allows changing decay rate over time in both C and S shape, while the peak value will always stay at the first period, meaning no lagged effect. PDF, or the probability density function, enables peak value occurring after the first period when shape >=1, allowing lagged effect. Run `plot_adstock()` to see the difference visually. Time estimation: with geometric adstock, 2000 iterations * 5 trials on 8 cores, it takes less than 30 minutes. Both Weibull options take up to twice as much time.
`hyperparameters`	List. Contains hyperparameter lower and upper bounds. Names of elements in list must be identical to output of `hyper_names()`. To fix hyperparameter values, provide only one value.
`window_start`, `window_end`	Character. Set start and end dates of modelling period. Recommended to not start in the first date in dataset to gain adstock effect from previous periods. Also, columns to rows ratio in the input data to be >=10:1, or in other words at least 10 observations to 1 independent variable. This window will determine the date range of the data period within your dataset you will be using to specifically regress the effects of media, organic and context variables on your dependent variable. We recommend using a full `dt_input` dataset with a minimum of 1 year of history, as it will be used in full for the model calculation of trend, seasonality and holidays effects. Whereas the window period will determine how much of the full data set will be used for media, organic and context variables.
`calibration_input`	data.frame. Optional. Provide experimental results to calibrate. Your input should include the following values for each experiment: channel, liftStartDate, liftEndDate, liftAbs, spend, confidence, metric. You can calibrate any spend or organic variable with a well designed experiment. You can also use experimental results from multiple channels; to do so, provide concatenated channel value, i.e. "channel_A+channel_B". Check "Guide for calibration source" section.
`json_file`	Character. JSON file to import previously exported inputs or recreate a model. To generate this file, use `robyn_write()`. If you didn't export your data in the json file as "raw_data", `dt_input` must be provided; `dt_holidays` input is optional.
`InputCollect`	Default to NULL. `robyn_inputs`'s output when `hyperparameters` are not yet set.
`...`	Additional parameters passed to `prophet` functions.
`x`	`robyn_inputs()` output.

Value

List. Contains all input parameters and modified results using Robyn:::robyn_engineering(). This list is ready to be used on other functions like robyn_run() and print(). Class: robyn_inputs.

Guide for calibration source

We strongly recommend to use experimental and causal results that are considered ground truth to calibrate MMM. Usual experiment types are people-based (e.g. Facebook conversion lift) and geo-based (e.g. Facebook GeoLift).
Currently, Robyn only accepts point-estimate as calibration input. For example, if 10k$ spend is tested against a hold-out for channel A, then input the incremental return as point-estimate as the example below.
The point-estimate has to always match the spend in the variable. For example, if channel A usually has 100k$ weekly spend and the experimental HO is 70

Examples

# Using dummy simulated data
InputCollect <- robyn_inputs(
  dt_input = Robyn::dt_simulated_weekly,
  dt_holidays = Robyn::dt_prophet_holidays,
  date_var = "DATE",
  dep_var = "revenue",
  dep_var_type = "revenue",
  prophet_vars = c("trend", "season", "holiday"),
  prophet_country = "DE",
  context_vars = c("competitor_sales_B", "events"),
  paid_media_spends = c("tv_S", "ooh_S", "print_S", "facebook_S", "search_S"),
  paid_media_vars = c("tv_S", "ooh_S", "print_S", "facebook_I", "search_clicks_P"),
  organic_vars = "newsletter",
  factor_vars = "events",
  window_start = "2016-11-23",
  window_end = "2018-08-22",
  adstock = "geometric",
  # To be defined separately
  hyperparameters = NULL,
  calibration_input = NULL
)
print(InputCollect)
# Using dummy simulated data
InputCollect <- robyn_inputs(
  dt_input = Robyn::dt_simulated_weekly,
  dt_holidays = Robyn::dt_prophet_holidays,
  date_var = "DATE",
  dep_var = "revenue",
  dep_var_type = "revenue",
  prophet_vars = c("trend", "season", "holiday"),
  prophet_country = "DE",
  context_vars = c("competitor_sales_B", "events"),
  paid_media_spends = c("tv_S", "ooh_S", "print_S", "facebook_S", "search_S"),
  paid_media_vars = c("tv_S", "ooh_S", "print_S", "facebook_I", "search_clicks_P"),
  organic_vars = "newsletter",
  factor_vars = "events",
  window_start = "2016-11-23",
  window_end = "2018-08-22",
  adstock = "geometric",
  # To be defined separately
  hyperparameters = NULL,
  calibration_input = NULL
)
print(InputCollect)

Core MMM Function

Description

robyn_mmm() function activates Nevergrad to generate samples of hyperparameters, conducts media transformation within each loop, fits the Ridge regression, calibrates the model optionally, decomposes responses and collects the result. It's an inner function within robyn_run().

Usage

robyn_mmm(
  InputCollect,
  hyper_collect,
  iterations,
  cores,
  nevergrad_algo,
  intercept = TRUE,
  intercept_sign,
  ts_validation = TRUE,
  add_penalty_factor = FALSE,
  objective_weights = NULL,
  dt_hyper_fixed = NULL,
  rssd_zero_penalty = TRUE,
  refresh = FALSE,
  trial = 1L,
  seed = 123L,
  quiet = FALSE,
  ...
)
robyn_mmm(
  InputCollect,
  hyper_collect,
  iterations,
  cores,
  nevergrad_algo,
  intercept = TRUE,
  intercept_sign,
  ts_validation = TRUE,
  add_penalty_factor = FALSE,
  objective_weights = NULL,
  dt_hyper_fixed = NULL,
  rssd_zero_penalty = TRUE,
  refresh = FALSE,
  trial = 1L,
  seed = 123L,
  quiet = FALSE,
  ...
)

Arguments

`InputCollect`	List. Contains all input parameters for the model. Required when `robyn_object` is not provided.
`hyper_collect`	List. Containing hyperparameter bounds. Defaults to `InputCollect$hyperparameters`.
`iterations`	Integer. Number of iterations to run.
`cores`	Integer. Default to `parallel::detectCores() - 1` (all cores except one). Set to 1 if you want to turn parallel computing off.
`nevergrad_algo`	Character. Default to "TwoPointsDE". Options are `c("DE","TwoPointsDE", "OnePlusOne", "DoubleFastGADiscreteOnePlusOne", "DiscreteOnePlusOne", "PortfolioDiscreteOnePlusOne", "NaiveTBPSA", "cGA", "RandomSearch")`.
`intercept`	Boolean. Should intercept(s) be fitted (default=TRUE) or set to zero (FALSE).
`intercept_sign`	Character. Choose one of "non_negative" (default) or "unconstrained". By default, if intercept is negative, Robyn will drop intercept and refit the model. Consider changing intercept_sign to "unconstrained" when there are `context_vars` with large positive values.
`ts_validation`	Boolean. When set to `TRUE`, Robyn will split data by test, train, and validation partitions to validate the time series. By default the "train_size" range is set to `c(0.5, 0.8)`, but it can be customized or set to a fixed value using the hyperparameters input. For example, if `train_size = 0.7`, validation size and test size will both be 0.15 and 0.15. When `ts_validation = FALSE`, nrmse_train is the objective function; when `ts_validation = TRUE`, nrmse_val is the objective function.
`add_penalty_factor`	Boolean. Add penalty factor hyperparameters to glmnet's penalty.factor to be optimized by nevergrad. Use with caution, because this feature might add too much hyperparameter space and probably requires more iterations to converge.
`objective_weights`	Numeric vector. Default to NULL to give equal weights to all objective functions. Order: NRMSE, DECOMP.RSSD, MAPE (when calibration data is provided). When you are not calibrating, only the first 2 values for `objective_weights` must be defined, i.e. set c(2, 1) to give double weight to the 1st (NRMSE). This is an experimental feature. There's no research on optimal weight setting. Subjective weights might strongly bias modeling results.
`dt_hyper_fixed`	data.frame or named list. Only provide when loading old model results. It consumes hyperparameters from saved csv `pareto_hyperparameters.csv` or JSON file to replicate a model.
`rssd_zero_penalty`	Boolean. When TRUE, the objective function DECOMP.RSSD will penalize models with more 0 media effects additionally. In other words, given the same DECOMP.RSSD score, a model with 50% 0-coef variables will get penalized by DECOMP.RSSD * 1.5 (larger error), while another model with no 0-coef variables gets un-penalized with DECOMP.RSSD * 1.
`refresh`	Boolean. Set to `TRUE` when used in `robyn_refresh()`.
`trial`	Integer. Which trial are we running? Used to ID each model.
`seed`	Integer. For reproducible results when running nevergrad.
`quiet`	Boolean. Keep messages off?
`...`	Additional parameters passed to `robyn_outputs()`.

Value

List. MMM results with hyperparameters values.

Evaluate Models and Output Results into Local Files

Description

Pack robyn_plots(), robyn_csv(), and robyn_clusters() outcomes on robyn_run() results. When UI=TRUE, enriched OutputModels results with additional plots and objects.

Create a plot to visualize the convergence for each of the datasets when running robyn_run(), especially useful for when using ts_validation. As a reference, the closer the test and validation convergence points are, the better, given the time-series wasn't overfitted.

Usage

robyn_outputs(
  InputCollect,
  OutputModels,
  pareto_fronts = "auto",
  calibration_constraint = 0.1,
  plot_folder = NULL,
  plot_folder_sub = NULL,
  plot_pareto = TRUE,
  csv_out = "pareto",
  clusters = TRUE,
  select_model = "clusters",
  ui = FALSE,
  export = TRUE,
  all_sol_json = FALSE,
  quiet = FALSE,
  refresh = FALSE,
  ...
)

## S3 method for class 'robyn_outputs'
print(x, ...)

robyn_csv(
  InputCollect,
  OutputCollect,
  csv_out = NULL,
  export = TRUE,
  calibrated = FALSE
)

robyn_plots(
  InputCollect,
  OutputCollect,
  export = TRUE,
  plot_folder = OutputCollect$plot_folder,
  ...
)

robyn_onepagers(
  InputCollect,
  OutputCollect,
  select_model = NULL,
  quiet = FALSE,
  export = TRUE,
  plot_folder = OutputCollect$plot_folder,
  baseline_level = 0,
  ...
)

ts_validation(OutputModels, quiet = FALSE, ...)

decomp_plot(
  InputCollect,
  OutputCollect,
  solID = NULL,
  exclude = NULL,
  baseline_level = 0
)
robyn_outputs(
  InputCollect,
  OutputModels,
  pareto_fronts = "auto",
  calibration_constraint = 0.1,
  plot_folder = NULL,
  plot_folder_sub = NULL,
  plot_pareto = TRUE,
  csv_out = "pareto",
  clusters = TRUE,
  select_model = "clusters",
  ui = FALSE,
  export = TRUE,
  all_sol_json = FALSE,
  quiet = FALSE,
  refresh = FALSE,
  ...
)

## S3 method for class 'robyn_outputs'
print(x, ...)

robyn_csv(
  InputCollect,
  OutputCollect,
  csv_out = NULL,
  export = TRUE,
  calibrated = FALSE
)

robyn_plots(
  InputCollect,
  OutputCollect,
  export = TRUE,
  plot_folder = OutputCollect$plot_folder,
  ...
)

robyn_onepagers(
  InputCollect,
  OutputCollect,
  select_model = NULL,
  quiet = FALSE,
  export = TRUE,
  plot_folder = OutputCollect$plot_folder,
  baseline_level = 0,
  ...
)

ts_validation(OutputModels, quiet = FALSE, ...)

decomp_plot(
  InputCollect,
  OutputCollect,
  solID = NULL,
  exclude = NULL,
  baseline_level = 0
)

Arguments

`InputCollect`, `OutputModels`	`robyn_inputs()` and `robyn_run()` outcomes.
`pareto_fronts`	Integer. Number of Pareto fronts for the output. `pareto_fronts = 1` returns the best models trading off `NRMSE` & `DECOMP.RSSD`. Increase `pareto_fronts` to get more model choices. `pareto_fronts = "auto"` selects the min fronts that include at least 100 candidates. To customize this threshold, set value with `min_candidates`.
`calibration_constraint`	Numeric. Default to 0.1 and allows 0.01-0.1. When calibrating, 0.1 means top 10 selection. Lower `calibration_constraint` increases calibration accuracy.
`plot_folder`	Character. Path for saving plots and files. Default to `robyn_object` and saves plot in the same directory as `robyn_object`.
`plot_folder_sub`	Character. Sub path for saving plots. Will overwrite the default path with timestamp or, for refresh and allocator, simply overwrite files.
`plot_pareto`	Boolean. Set to `FALSE` to deactivate plotting and saving model one-pagers. Used when testing models.
`csv_out`	Character. Accepts "pareto" or "all". Default to "pareto". Set to "all" will output all iterations as csv. Set NULL to skip exports into CSVs.
`clusters`	Boolean. Apply `robyn_clusters()` to output models?
`select_model`	Character vector. Which models (by `solID`) do you wish to plot the one-pagers and export? Default will take top `robyn_clusters()` results.
`ui`	Boolean. Save additional outputs for UI usage. List outcome.
`export`	Boolean. Export outcomes into local files?
`all_sol_json`	Logical. Add all pareto solutions to json export?
`quiet`	Boolean. Keep messages off?
`refresh`	Boolean. Refresh mode
`...`	Additional parameters passed to `robyn_clusters()`
`x`	`robyn_outputs()` output.
`OutputCollect`	`robyn_run(..., export = FALSE)` output.
`calibrated`	Logical
`baseline_level`	Integer, from 0 to 5. Aggregate baseline variables, depending on the level of aggregation you need. Default is 0 for no aggregation. 1 for Intercept only. 2 adding trend. 3 adding all prophet decomposition variables. 4. Adding contextual variables. 5 Adding organic variables. Results will be reflected on the waterfall chart.
`solID`	Character vector. Model IDs to plot.
`exclude`	Character vector. Manually exclude variables from plot.

Value

(Invisible) list. Class: robyn_outputs. Contains processed results based on robyn_run() results.

Invisible NULL.

Invisible list with ggplot plots.

Invisible list with patchwork plot(s).

Invisible list with ggplot plots.

Build Refresh Model

Description

robyn_refresh() builds updated models based on the previously built models saved in the Robyn.RDS object specified in robyn_object. For example, when updating the initial build with 4 weeks of new data, robyn_refresh() consumes the selected model of the initial build, sets lower and upper bounds of hyperparameters for the new build around the selected hyperparameters of the previous build, stabilizes the effect of baseline variables across old and new builds, and regulates the new effect share of media variables towards the latest spend level. It returns the aggregated results with all previous builds for reporting purposes and produces reporting plots.

You must run robyn_save() to select and save an initial model first, before refreshing.

When should robyn_refresh() NOT be used: The robyn_refresh() function is suitable for updating within "reasonable periods". Two situations are considered better to rebuild model instead of refreshing:

1. Most data is new: If initial model was trained with 100 weeks worth of data but we add +50 weeks of new data.

2. New variables are added: If initial model had less variables than the ones we want to start using on new refresh model.

Usage

robyn_refresh(
  json_file = NULL,
  robyn_object = NULL,
  dt_input = NULL,
  dt_holidays = Robyn::dt_prophet_holidays,
  refresh_steps = 4,
  refresh_mode = "manual",
  refresh_iters = 1000,
  refresh_trials = 3,
  bounds_freedom = NULL,
  plot_folder = NULL,
  plot_pareto = TRUE,
  version_prompt = FALSE,
  export = TRUE,
  calibration_input = NULL,
  objective_weights = NULL,
  ...
)

## S3 method for class 'robyn_refresh'
print(x, ...)

## S3 method for class 'robyn_refresh'
plot(x, ...)
robyn_refresh(
  json_file = NULL,
  robyn_object = NULL,
  dt_input = NULL,
  dt_holidays = Robyn::dt_prophet_holidays,
  refresh_steps = 4,
  refresh_mode = "manual",
  refresh_iters = 1000,
  refresh_trials = 3,
  bounds_freedom = NULL,
  plot_folder = NULL,
  plot_pareto = TRUE,
  version_prompt = FALSE,
  export = TRUE,
  calibration_input = NULL,
  objective_weights = NULL,
  ...
)

## S3 method for class 'robyn_refresh'
print(x, ...)

## S3 method for class 'robyn_refresh'
plot(x, ...)

Arguments

`json_file`	Character. JSON file to import previously exported inputs or recreate a model. To generate this file, use `robyn_write()`. If you didn't export your data in the json file as "raw_data", `dt_input` must be provided; `dt_holidays` input is optional.
`robyn_object`	Character or List. Path of the `Robyn.RDS` object that contains all previous modeling information or the imported list.
`dt_input`	data.frame. Should include all previous data and newly added data for the refresh.
`dt_holidays`	data.frame. Raw input holiday data. Load standard Prophet holidays using `data("dt_prophet_holidays")`.
`refresh_steps`	Integer. It controls how many time units the refresh model build move forward. For example, `refresh_steps = 4` on weekly data means the `InputCollect$window_start` & `InputCollect$window_end` move forward 4 weeks. If `refresh_steps` is smaller than the number of newly provided data points, then Robyn would only use the first N steps of the new data.
`refresh_mode`	Character. Options are "auto" and "manual". In auto mode, the `robyn_refresh()` function builds refresh models with given `refresh_steps` repeatedly until there's no more data available. I manual mode, the `robyn_refresh()` only moves forward `refresh_steps` only once. "auto" mode has been deprecated when using `json_file` input.
`refresh_iters`	Integer. Iterations per refresh. Rule of thumb is, the more new data added, the more iterations needed. More reliable recommendation still needs to be investigated.
`refresh_trials`	Integer. Trials per refresh. Defaults to 5 trials. More reliable recommendation still needs to be investigated.
`bounds_freedom`	Numeric. Percentage of freedom we'd like to allow for the new hyperparameters values compared with the model to be refreshed. If set to NULL (default) the value will be calculated as refresh_steps / rollingWindowLength. Applies to all hyperparameters.
`plot_folder`	Character. Path for saving plots and files. Default to `robyn_object` and saves plot in the same directory as `robyn_object`.
`plot_pareto`	Boolean. Set to `FALSE` to deactivate plotting and saving model one-pagers. Used when testing models.
`version_prompt`	Logical. If FALSE, the model refresh version will be selected based on the smallest combined error of normalized NRMSE, DECOMP.RSSD, MAPE. If `TRUE`, a prompt will be presented to the user to select one of the refreshed models (one-pagers and Pareto CSV files will already be generated).
`export`	Boolean. Export outcomes into local files?
`calibration_input`	data.frame. Optional. Provide experimental results to calibrate. Your input should include the following values for each experiment: channel, liftStartDate, liftEndDate, liftAbs, spend, confidence, metric. You can calibrate any spend or organic variable with a well designed experiment. You can also use experimental results from multiple channels; to do so, provide concatenated channel value, i.e. "channel_A+channel_B". Check "Guide for calibration source" section.
`objective_weights`	Numeric vector. Default to NULL to give equal weights to all objective functions. Order: NRMSE, DECOMP.RSSD, MAPE (when calibration data is provided). When you are not calibrating, only the first 2 values for `objective_weights` must be defined, i.e. set c(2, 1) to give double weight to the 1st (NRMSE). This is an experimental feature. There's no research on optimal weight setting. Subjective weights might strongly bias modeling results.
`...`	Additional parameters to overwrite original custom parameters passed into initial model.
`x`	`robyn_refresh()` output.

Value

List. The Robyn object, class robyn_refresh.

List. Same as robyn_run() but with refreshed models.

Examples

## Not run: 
# Loading dummy data
data("dt_simulated_weekly")
data("dt_prophet_holidays")
# Set the (pre-trained and exported) Robyn model JSON file
json_file <- "~/Robyn_202208081444_init/RobynModel-2_55_4.json"

# Run \code{robyn_refresh()} with 13 weeks cadence in auto mode
Robyn <- robyn_refresh(
  json_file = json_file,
  dt_input = dt_simulated_weekly,
  dt_holidays = Robyn::dt_prophet_holidays,
  refresh_steps = 13,
  refresh_mode = "auto",
  refresh_iters = 200,
  refresh_trials = 5
)

# Run \code{robyn_refresh()} with 4 weeks cadence in manual mode
json_file2 <- "~/Robyn_202208081444_init/Robyn_202208090847_rf/RobynModel-1_2_3.json"
Robyn <- robyn_refresh(
  json_file = json_file2,
  dt_input = dt_simulated_weekly,
  dt_holidays = Robyn::dt_prophet_holidays,
  refresh_steps = 4,
  refresh_mode = "manual",
  refresh_iters = 200,
  refresh_trials = 5
)

## End(Not run)
## Not run: 
# Loading dummy data
data("dt_simulated_weekly")
data("dt_prophet_holidays")
# Set the (pre-trained and exported) Robyn model JSON file
json_file <- "~/Robyn_202208081444_init/RobynModel-2_55_4.json"

# Run \code{robyn_refresh()} with 13 weeks cadence in auto mode
Robyn <- robyn_refresh(
  json_file = json_file,
  dt_input = dt_simulated_weekly,
  dt_holidays = Robyn::dt_prophet_holidays,
  refresh_steps = 13,
  refresh_mode = "auto",
  refresh_iters = 200,
  refresh_trials = 5
)

# Run \code{robyn_refresh()} with 4 weeks cadence in manual mode
json_file2 <- "~/Robyn_202208081444_init/Robyn_202208090847_rf/RobynModel-1_2_3.json"
Robyn <- robyn_refresh(
  json_file = json_file2,
  dt_input = dt_simulated_weekly,
  dt_holidays = Robyn::dt_prophet_holidays,
  refresh_steps = 4,
  refresh_mode = "manual",
  refresh_iters = 200,
  refresh_trials = 5
)

## End(Not run)

Response and Saturation Curves

Description

robyn_response() returns the response for a given spend level of a given paid_media_vars from a selected model result and selected model build (initial model, refresh model, etc.).

Usage

robyn_response(
  InputCollect = NULL,
  OutputCollect = NULL,
  json_file = NULL,
  robyn_object = NULL,
  select_build = NULL,
  select_model = NULL,
  metric_name = NULL,
  metric_value = NULL,
  date_range = NULL,
  dt_hyppar = NULL,
  dt_coef = NULL,
  quiet = FALSE,
  ...
)
robyn_response(
  InputCollect = NULL,
  OutputCollect = NULL,
  json_file = NULL,
  robyn_object = NULL,
  select_build = NULL,
  select_model = NULL,
  metric_name = NULL,
  metric_value = NULL,
  date_range = NULL,
  dt_hyppar = NULL,
  dt_coef = NULL,
  quiet = FALSE,
  ...
)

Arguments

`InputCollect`	List. Contains all input parameters for the model. Required when `robyn_object` is not provided.
`OutputCollect`	List. Containing all model result. Required when `robyn_object` is not provided.
`json_file`	Character. JSON file to import previously exported inputs or recreate a model. To generate this file, use `robyn_write()`. If you didn't export your data in the json file as "raw_data", `dt_input` must be provided; `dt_holidays` input is optional.
`robyn_object`	Character or List. Path of the `Robyn.RDS` object that contains all previous modeling information or the imported list.
`select_build`	Integer. Default to the latest model build. `select_build = 0` selects the initial model. `select_build = 1` selects the first refresh model.
`select_model`	Character. A model `SolID`. When `robyn_object` is provided, `select_model` defaults to the already selected `SolID`. When `robyn_object` is not provided, `select_model` must be provided with `InputCollect` and `OutputCollect`, and must be one of `OutputCollect$allSolutions`.
`metric_name`	A character. Selected media variable for the response. Must be one value from paid_media_spends, paid_media_vars or organic_vars
`metric_value`	Numeric. Desired metric value to return a response for.
`date_range`	Character. Date(s) to apply adstocked transformations and pick mean spends per channel. Set one of: "all", "last", or "last_n" (where n is the last N dates available), date (i.e. "2022-03-27"), or date range (i.e. `c("2022-01-01", "2022-12-31")`). Default to "all".
`dt_hyppar`	A data.frame. When `robyn_object` is not provided, use `dt_hyppar = OutputCollect$resultHypParam`. It must be provided along `select_model`, `dt_coef` and `InputCollect`.
`dt_coef`	A data.frame. When `robyn_object` is not provided, use `dt_coef = OutputCollect$xDecompAgg`. It must be provided along `select_model`, `dt_hyppar` and `InputCollect`.
`quiet`	Boolean. Keep messages off?
`...`	Additional parameters passed to `robyn_outputs()`.

Value

List. Response value and plot. Class: robyn_response.

Examples

## Not run: 
# Having InputCollect and OutputCollect objects
## Recreate original saturation curve
Response <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "facebook_S"
)
Response$plot

## Or you can call a JSON file directly (a bit slower)
# Response <- robyn_response(
#   json_file = "your_json_path.json",
#   dt_input = dt_simulated_weekly,
#   dt_holidays = dt_prophet_holidays,
#   metric_name = "facebook_S"
# )

## Get the "next 100 dollar" marginal response on Spend1
Spend1 <- 20000
Response1 <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "facebook_S",
  metric_value = Spend1, # total budget for date_range
  date_range = "last_1" # last two periods
)
Response1$plot

Spend2 <- Spend1 + 100
Response2 <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "facebook_S",
  metric_value = Spend2,
  date_range = "last_1"
)
# ROAS for the 100$ from Spend1 level
(Response2$response_total - Response1$response_total) / (Spend2 - Spend1)

## Get response from for a given budget and date_range
Spend3 <- 100000
Response3 <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "facebook_S",
  metric_value = Spend3, # total budget for date_range
  date_range = "last_5" # last 5 periods
)
Response3$plot

## Example of getting paid media exposure response curves
imps <- 10000000
response_imps <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "facebook_I",
  metric_value = imps
)
response_imps$response_total / imps * 1000
response_imps$plot

## Example of getting organic media exposure response curves
sendings <- 30000
response_sending <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "newsletter",
  metric_value = sendings
)
# response per 1000 sendings
response_sending$response_total / sendings * 1000
response_sending$plot

## End(Not run)
## Not run: 
# Having InputCollect and OutputCollect objects
## Recreate original saturation curve
Response <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "facebook_S"
)
Response$plot

## Or you can call a JSON file directly (a bit slower)
# Response <- robyn_response(
#   json_file = "your_json_path.json",
#   dt_input = dt_simulated_weekly,
#   dt_holidays = dt_prophet_holidays,
#   metric_name = "facebook_S"
# )

## Get the "next 100 dollar" marginal response on Spend1
Spend1 <- 20000
Response1 <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "facebook_S",
  metric_value = Spend1, # total budget for date_range
  date_range = "last_1" # last two periods
)
Response1$plot

Spend2 <- Spend1 + 100
Response2 <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "facebook_S",
  metric_value = Spend2,
  date_range = "last_1"
)
# ROAS for the 100$ from Spend1 level
(Response2$response_total - Response1$response_total) / (Spend2 - Spend1)

## Get response from for a given budget and date_range
Spend3 <- 100000
Response3 <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "facebook_S",
  metric_value = Spend3, # total budget for date_range
  date_range = "last_5" # last 5 periods
)
Response3$plot

## Example of getting paid media exposure response curves
imps <- 10000000
response_imps <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "facebook_I",
  metric_value = imps
)
response_imps$response_total / imps * 1000
response_imps$plot

## Example of getting organic media exposure response curves
sendings <- 30000
response_sending <- robyn_response(
  InputCollect = InputCollect,
  OutputCollect = OutputCollect,
  select_model = select_model,
  metric_name = "newsletter",
  metric_value = sendings
)
# response per 1000 sendings
response_sending$response_total / sendings * 1000
response_sending$plot

## End(Not run)

Robyn Modelling Function

Description

robyn_run() consumes robyn_input() outputs, runs robyn_mmm(), and collects all modeling results.

Usage

robyn_run(
  InputCollect = NULL,
  dt_hyper_fixed = NULL,
  json_file = NULL,
  ts_validation = FALSE,
  add_penalty_factor = FALSE,
  refresh = FALSE,
  seed = 123L,
  quiet = FALSE,
  cores = NULL,
  trials = 5,
  iterations = 2000,
  rssd_zero_penalty = TRUE,
  objective_weights = NULL,
  nevergrad_algo = "TwoPointsDE",
  intercept = TRUE,
  intercept_sign = "non_negative",
  lambda_control = NULL,
  outputs = FALSE,
  ...
)

## S3 method for class 'robyn_models'
print(x, ...)
robyn_run(
  InputCollect = NULL,
  dt_hyper_fixed = NULL,
  json_file = NULL,
  ts_validation = FALSE,
  add_penalty_factor = FALSE,
  refresh = FALSE,
  seed = 123L,
  quiet = FALSE,
  cores = NULL,
  trials = 5,
  iterations = 2000,
  rssd_zero_penalty = TRUE,
  objective_weights = NULL,
  nevergrad_algo = "TwoPointsDE",
  intercept = TRUE,
  intercept_sign = "non_negative",
  lambda_control = NULL,
  outputs = FALSE,
  ...
)

## S3 method for class 'robyn_models'
print(x, ...)

Arguments

`InputCollect`	List. Contains all input parameters for the model. Required when `robyn_object` is not provided.
`dt_hyper_fixed`	data.frame or named list. Only provide when loading old model results. It consumes hyperparameters from saved csv `pareto_hyperparameters.csv` or JSON file to replicate a model.
`json_file`	Character. JSON file to import previously exported inputs or recreate a model. To generate this file, use `robyn_write()`. If you didn't export your data in the json file as "raw_data", `dt_input` must be provided; `dt_holidays` input is optional.
`ts_validation`	Boolean. When set to `TRUE`, Robyn will split data by test, train, and validation partitions to validate the time series. By default the "train_size" range is set to `c(0.5, 0.8)`, but it can be customized or set to a fixed value using the hyperparameters input. For example, if `train_size = 0.7`, validation size and test size will both be 0.15 and 0.15. When `ts_validation = FALSE`, nrmse_train is the objective function; when `ts_validation = TRUE`, nrmse_val is the objective function.
`add_penalty_factor`	Boolean. Add penalty factor hyperparameters to glmnet's penalty.factor to be optimized by nevergrad. Use with caution, because this feature might add too much hyperparameter space and probably requires more iterations to converge.
`refresh`	Boolean. Set to `TRUE` when used in `robyn_refresh()`.
`seed`	Integer. For reproducible results when running nevergrad.
`quiet`	Boolean. Keep messages off?
`cores`	Integer. Default to `parallel::detectCores() - 1` (all cores except one). Set to 1 if you want to turn parallel computing off.
`trials`	Integer. Recommended 5 for default `nevergrad_algo = "TwoPointsDE"`.
`iterations`	Integer. Recommended 2000 for default when using `nevergrad_algo = "TwoPointsDE"`.
`rssd_zero_penalty`	Boolean. When TRUE, the objective function DECOMP.RSSD will penalize models with more 0 media effects additionally. In other words, given the same DECOMP.RSSD score, a model with 50% 0-coef variables will get penalized by DECOMP.RSSD * 1.5 (larger error), while another model with no 0-coef variables gets un-penalized with DECOMP.RSSD * 1.
`objective_weights`	Numeric vector. Default to NULL to give equal weights to all objective functions. Order: NRMSE, DECOMP.RSSD, MAPE (when calibration data is provided). When you are not calibrating, only the first 2 values for `objective_weights` must be defined, i.e. set c(2, 1) to give double weight to the 1st (NRMSE). This is an experimental feature. There's no research on optimal weight setting. Subjective weights might strongly bias modeling results.
`nevergrad_algo`	Character. Default to "TwoPointsDE". Options are `c("DE","TwoPointsDE", "OnePlusOne", "DoubleFastGADiscreteOnePlusOne", "DiscreteOnePlusOne", "PortfolioDiscreteOnePlusOne", "NaiveTBPSA", "cGA", "RandomSearch")`.
`intercept`	Boolean. Should intercept(s) be fitted (default=TRUE) or set to zero (FALSE).
`intercept_sign`	Character. Choose one of "non_negative" (default) or "unconstrained". By default, if intercept is negative, Robyn will drop intercept and refit the model. Consider changing intercept_sign to "unconstrained" when there are `context_vars` with large positive values.
`lambda_control`	Deprecated in v3.6.0.
`outputs`	Boolean. If set to TRUE, will run `robyn_run()` and `robyn_outputs()`, returning a list with OutputModels and OutputCollect results.
`...`	Additional parameters passed to `robyn_outputs()`.
`x`	`robyn_models()` output.

Value

List. Class: robyn_models. Contains the results of all trials and iterations modeled.

List. Contains all trained models. Class: robyn_models.

Examples

## Not run: 
# Having InputCollect results
OutputModels <- robyn_run(
  InputCollect = InputCollect,
  cores = 2,
  iterations = 200,
  trials = 1
)

## End(Not run)
## Not run: 
# Having InputCollect results
OutputModels <- robyn_run(
  InputCollect = InputCollect,
  cores = 2,
  iterations = 200,
  trials = 1
)

## End(Not run)

Export Robyn Model to Local File [DEPRECATED]

Description

Use robyn_save() to select and save as .RDS file the initial model.

Usage

robyn_save(
  InputCollect,
  OutputCollect,
  robyn_object = NULL,
  select_model = NULL,
  dir = OutputCollect$plot_folder,
  quiet = FALSE,
  ...
)

## S3 method for class 'robyn_save'
print(x, ...)

## S3 method for class 'robyn_save'
plot(x, ...)

robyn_load(robyn_object, select_build = NULL, quiet = FALSE)
robyn_save(
  InputCollect,
  OutputCollect,
  robyn_object = NULL,
  select_model = NULL,
  dir = OutputCollect$plot_folder,
  quiet = FALSE,
  ...
)

## S3 method for class 'robyn_save'
print(x, ...)

## S3 method for class 'robyn_save'
plot(x, ...)

robyn_load(robyn_object, select_build = NULL, quiet = FALSE)

Arguments

`InputCollect`	List. Contains all input parameters for the model. Required when `robyn_object` is not provided.
`OutputCollect`	List. Containing all model result. Required when `robyn_object` is not provided.
`robyn_object`	Character or List. Path of the `Robyn.RDS` object that contains all previous modeling information or the imported list.
`select_model`	Character. A model `SolID`. When `robyn_object` is provided, `select_model` defaults to the already selected `SolID`. When `robyn_object` is not provided, `select_model` must be provided with `InputCollect` and `OutputCollect`, and must be one of `OutputCollect$allSolutions`.
`dir`	Character. Existing directory to export JSON file to.
`quiet`	Boolean. Keep messages off?
`...`	Additional parameters passed to `robyn_outputs()`.
`x`	`robyn_save()` output.
`select_build`	Integer. Default to the latest model build. `select_build = 0` selects the initial model. `select_build = 1` selects the first refresh model.

Value

(Invisible) list with filename and summary. Class: robyn_save.

(Invisible) list with imported results

Train Robyn Models

Description

robyn_train() consumes output from robyn_input() and runs the robyn_mmm() on each trial.

Usage

robyn_train(
  InputCollect,
  hyper_collect,
  cores,
  iterations,
  trials,
  intercept_sign,
  intercept,
  nevergrad_algo,
  dt_hyper_fixed = NULL,
  ts_validation = TRUE,
  add_penalty_factor = FALSE,
  objective_weights = NULL,
  rssd_zero_penalty = TRUE,
  refresh = FALSE,
  seed = 123,
  quiet = FALSE
)
robyn_train(
  InputCollect,
  hyper_collect,
  cores,
  iterations,
  trials,
  intercept_sign,
  intercept,
  nevergrad_algo,
  dt_hyper_fixed = NULL,
  ts_validation = TRUE,
  add_penalty_factor = FALSE,
  objective_weights = NULL,
  rssd_zero_penalty = TRUE,
  refresh = FALSE,
  seed = 123,
  quiet = FALSE
)

Arguments

`InputCollect`	List. Contains all input parameters for the model. Required when `robyn_object` is not provided.
`hyper_collect`	List. Containing hyperparameter bounds. Defaults to `InputCollect$hyperparameters`.
`cores`	Integer. Default to `parallel::detectCores() - 1` (all cores except one). Set to 1 if you want to turn parallel computing off.
`iterations`	Integer. Recommended 2000 for default when using `nevergrad_algo = "TwoPointsDE"`.
`trials`	Integer. Recommended 5 for default `nevergrad_algo = "TwoPointsDE"`.
`intercept_sign`	Character. Choose one of "non_negative" (default) or "unconstrained". By default, if intercept is negative, Robyn will drop intercept and refit the model. Consider changing intercept_sign to "unconstrained" when there are `context_vars` with large positive values.
`intercept`	Boolean. Should intercept(s) be fitted (default=TRUE) or set to zero (FALSE).
`nevergrad_algo`	Character. Default to "TwoPointsDE". Options are `c("DE","TwoPointsDE", "OnePlusOne", "DoubleFastGADiscreteOnePlusOne", "DiscreteOnePlusOne", "PortfolioDiscreteOnePlusOne", "NaiveTBPSA", "cGA", "RandomSearch")`.
`dt_hyper_fixed`	data.frame or named list. Only provide when loading old model results. It consumes hyperparameters from saved csv `pareto_hyperparameters.csv` or JSON file to replicate a model.
`ts_validation`	Boolean. When set to `TRUE`, Robyn will split data by test, train, and validation partitions to validate the time series. By default the "train_size" range is set to `c(0.5, 0.8)`, but it can be customized or set to a fixed value using the hyperparameters input. For example, if `train_size = 0.7`, validation size and test size will both be 0.15 and 0.15. When `ts_validation = FALSE`, nrmse_train is the objective function; when `ts_validation = TRUE`, nrmse_val is the objective function.
`add_penalty_factor`	Boolean. Add penalty factor hyperparameters to glmnet's penalty.factor to be optimized by nevergrad. Use with caution, because this feature might add too much hyperparameter space and probably requires more iterations to converge.
`objective_weights`	Numeric vector. Default to NULL to give equal weights to all objective functions. Order: NRMSE, DECOMP.RSSD, MAPE (when calibration data is provided). When you are not calibrating, only the first 2 values for `objective_weights` must be defined, i.e. set c(2, 1) to give double weight to the 1st (NRMSE). This is an experimental feature. There's no research on optimal weight setting. Subjective weights might strongly bias modeling results.
`rssd_zero_penalty`	Boolean. When TRUE, the objective function DECOMP.RSSD will penalize models with more 0 media effects additionally. In other words, given the same DECOMP.RSSD score, a model with 50% 0-coef variables will get penalized by DECOMP.RSSD * 1.5 (larger error), while another model with no 0-coef variables gets un-penalized with DECOMP.RSSD * 1.
`refresh`	Boolean. Set to `TRUE` when used in `robyn_refresh()`.
`seed`	Integer. For reproducible results when running nevergrad.
`quiet`	Boolean. Keep messages off?

Value

List. Iteration results to include in robyn_run() results.

Update Robyn Version

Description

Update Robyn version from Github repository for latest "dev" version or from CRAN for latest "stable" version.

Usage

robyn_update(dev = TRUE, ...)
robyn_update(dev = TRUE, ...)

Arguments

`dev`	Boolean. Dev version? If not, CRAN version.
`...`	Parameters to pass to `remotes::install_github` or `utils::install.packages`, depending on `dev` parameter.

Value

Invisible NULL.

Import and Export Robyn JSON files

Description

robyn_write() generates light JSON files with all the information required to replicate Robyn models. Depending on user inputs, there are 3 use cases: only the inputs data, input data + modeling results data, and input data, modeling results + specifics of a single selected model. To replicate a model, you must provide InputCollect, OutputCollect, and, if OutputCollect contains more than one model, the select_model.

Usage

robyn_write(
  InputCollect,
  OutputCollect = NULL,
  select_model = NULL,
  dir = OutputCollect$plot_folder,
  add_data = TRUE,
  export = TRUE,
  quiet = FALSE,
  pareto_df = NULL,
  ...
)

## S3 method for class 'robyn_write'
print(x, ...)

robyn_read(json_file = NULL, step = 1, quiet = FALSE, ...)

## S3 method for class 'robyn_read'
print(x, ...)

robyn_recreate(json_file, quiet = FALSE, ...)
robyn_write(
  InputCollect,
  OutputCollect = NULL,
  select_model = NULL,
  dir = OutputCollect$plot_folder,
  add_data = TRUE,
  export = TRUE,
  quiet = FALSE,
  pareto_df = NULL,
  ...
)

## S3 method for class 'robyn_write'
print(x, ...)

robyn_read(json_file = NULL, step = 1, quiet = FALSE, ...)

## S3 method for class 'robyn_read'
print(x, ...)

robyn_recreate(json_file, quiet = FALSE, ...)

Arguments

`InputCollect`	`robyn_inputs()` output.
`OutputCollect`	`robyn_run(..., export = FALSE)` output.
`select_model`	Character. Which model ID do you want to export into the JSON file?
`dir`	Character. Existing directory to export JSON file to.
`add_data`	Boolean. Include raw dataset. Useful to recreate models with a single file containing all the required information (no need of CSV).
`export`	Boolean. Export outcomes into local files?
`quiet`	Boolean. Keep messages off?
`pareto_df`	Dataframe. Save all pareto solutions to json file.
`...`	Additional parameters to export into a custom Extras element.
`x`	`robyn_read()` or `robyn_write()` output.
`json_file`	Character. JSON file name to read and import.
`step`	Integer. 1 for import only and 2 for import and output.

Value

(invisible) List. Contains all inputs and outputs of exported model. Class: robyn_write.

Examples

## Not run: 
InputCollectJSON <- robyn_inputs(
  dt_input = Robyn::dt_simulated_weekly,
  json_file = "~/Desktop/RobynModel-1_29_12.json"
)
print(InputCollectJSON)

## End(Not run)
## Not run: 
InputCollectJSON <- robyn_inputs(
  dt_input = Robyn::dt_simulated_weekly,
  json_file = "~/Desktop/RobynModel-1_29_12.json"
)
print(InputCollectJSON)

## End(Not run)

Hill Saturation Transformation

Description

saturation_hill is a two-parametric version of the Hill function that allows the saturation curve to flip between S and C shape.

Produce example plots for the Hill saturation curve.

Usage

saturation_hill(x, alpha, gamma, x_marginal = NULL)

plot_saturation(plot = TRUE)
saturation_hill(x, alpha, gamma, x_marginal = NULL)

plot_saturation(plot = TRUE)

Arguments

`x`	Numeric vector.
`alpha`	Numeric. Alpha controls the shape of the saturation curve. The larger the alpha, the more S-shape. The smaller, the more C-shape.
`gamma`	Numeric. Gamma controls the inflexion point of the saturation curve. The larger the gamma, the later the inflexion point occurs.
`x_marginal`	Numeric. When provided, the function returns the Hill-transformed value of the x_marginal input.
`plot`	Boolean. Do you wish to return the plot?

Value

Numeric values. Transformed values.

Examples

saturation_hill(c(100, 150, 170, 190, 200), alpha = 3, gamma = 0.5)
saturation_hill(c(100, 150, 170, 190, 200), alpha = 3, gamma = 0.5)

Detect and set date variable interval

Description

Robyn only accepts daily, weekly and monthly data. This function is only called in robyn_engineering().

Usage

set_holidays(dt_transform, dt_holidays, intervalType)
set_holidays(dt_transform, dt_holidays, intervalType)

Arguments

`dt_transform`	A data.frame. Transformed input data.
`dt_holidays`	A data.frame. Raw input holiday data.
`intervalType`	A character. Accepts one of the values: `c("day","week","month")`

Value

List. Containing the all spend-exposure model results.

Package 'Robyn'

Help Index

Adstocking Transformation (Geometric and Weibull)

Description

Usage

Arguments

Details

Value

See Also

Examples

Robyn Dataset: Holidays by Country

Description

Usage

Format

Value

See Also

Examples

Robyn Dataset: MMM Demo Data

Description

Usage

Format

Value

See Also

Examples

Fit a nonlinear model for media spend and exposure

Description

Usage

Arguments

Value

Check hyperparameter limits

Description

Usage

Value

Examples

Get correct hyperparameter names

Description

Usage

Arguments

Value

Guide to setup hyperparameters

Helper plots

Examples

Michaelis-Menten Transformation

Description

Usage

Arguments

Value

See Also

Examples

Conduct prophet decomposition

Description

Usage

Arguments

Value

Robyn MMM Project from Meta Marketing Science

Description

Author(s)

See Also

Budget Allocator

Description

Usage

Arguments

Value

Examples

Clustering to Reduce Number of Models based on ROI and Errors

Description

Usage

Arguments

Value

Author(s)

Examples

Check Models Convergence

Description

Usage

Arguments

Value

Examples

Input Data Check & Transformation

Description

Usage