predict_ses: Use simple exponential smoothing to forecast data

In caldwellst/augury: Provides Streamlined Methods for Data Imputation and Forecasting for WHO DDI Statistics

Use simple exponential smoothing to forecast data

Description

predict_ses() is a simple wrapper that uses simple exponential smoothing to forecast data. For details surrounding the model fitting, please see forecast::holt() and for more details on the augury function this wraps around and the various arguments this function accepts, please see predict_forecast().

Usage

predict_ses(
  df,
  response,
  ...,
  ret = c("df", "all", "error", "model"),
  scale = NULL,
  probit = FALSE,
  test_col = NULL,
  group_col = "iso3",
  group_models = TRUE,
  obs_filter = NULL,
  sort_col = "year",
  sort_descending = FALSE,
  pred_col = "pred",
  pred_upper_col = "pred_upper",
  pred_lower_col = "pred_lower",
  upper_col = "upper",
  lower_col = "lower",
  filter_na = c("all", "response", "predictors", "none"),
  type_col = NULL,
  types = "projected",
  source_col = NULL,
  source = NULL,
  scenario_detail_col = NULL,
  scenario_detail = NULL,
  replace_obs = c("missing", "all", "none")
)

Arguments

df	Data frame of model data.
response	Column name of response variable to be used as the input to the forecast function.
...	Additional arguments passed to the forecast function.
ret	Character vector specifying what values the function returns. Defaults to returning a data frame, but can return a vector of model error, the model itself or a list with all 3 as components.
scale	Either NULL or a numeric value. If a numeric value is provided, the response variable is scaled by the value passed to scale prior to model fitting and prior to any probit transformation, so can be used to put the response onto a 0 to 1 scale. Scaling is done by dividing the response by the scale and using the scale_transform() function. The response, as well as the fitted values and confidence bounds are unscaled prior to error calculation and returning to the user.
probit	Logical value on whether or not to probit transform the response prior to model fitting. Probit transformation is performed after any scaling determined by scale but prior to model fitting. The response, as well as the fitted values and confidence bounds are untransformed prior to error calculation and returning to the user.
test_col	Name of logical column specifying which response values to remove for testing the model's predictive accuracy. If NULL, ignored. See model_error() for details on the methods and metrics returned.
group_col	Column name(s) of group(s) to use in dplyr::group_by() when supplying type, calculating mean absolute scaled error on data involving time series, and if group_models, then fitting and predicting models too. If NULL, not used. Defaults to "iso3".
group_models	Logical, if TRUE, fits and predicts models individually onto each group_col. If FALSE, a general model is fit across the entire data frame.
obs_filter	String value of the form "⁠logical operator⁠ integer" that specifies the number of observations required to fit the model and replace observations with predicted values. This is done in conjunction with group_col. So, if group_col = "iso3" and obs_filter = ">= 5", then for this model, predictions will only be used for iso3 vales that have 5 or more observations. Possible logical operators to use are >, >=, <, <=, ==, and !=. If `group_models = FALSE`, then `obs_filter` is only used to determine when predicted values replace observed values but **is not** used to restrict values from being used in model fitting. If `group_models = TRUE`, then a model is only fit for a group if they meet the `obs_filter` requirements. This provides speed benefits, particularly when running INLA time series using `predict_inla()`.
sort_col	Column name of column to arrange data by in dplyr::arrange(), prior to filtering for latest contiguous time series and producing the forecast. Not used if NULL, defaults to "year".
sort_descending	Logical value on whether the sorted values from sort_col should be sorted in descending order. Defaults to FALSE.
pred_col	Column name to store predicted value.
pred_upper_col	Column name to store upper bound of confidence interval generated by the predict_... function. This stores the full set of generated values for the upper bound.
pred_lower_col	Column name to store lower bound of confidence interval generated by the predict_... function. This stores the full set of generated values for the lower bound.
upper_col	Column name that contains upper bound information, including upper bound of the input data to the model. Values from pred_upper_col are put into this column in the exact same way the response is filled by pred based on replace_na (only when there is a missing value in the response).
lower_col	Column name that contains lower bound information, including lower bound of the input data to the model. Values from pred_lower_col are put into this column in the exact same way the response is filled by pred based on replace_na (only when there is a missing value in the response).
filter_na	Character value specifying how, if at all, to filter NA values from the dataset prior to applying the model. By default, all observations with missing values are removed, although it can also remove rows only if they have missing dependent or independent variables, or no filtering at all.
type_col	Column name specifying data type.
types	Vector of length 3 that provides the type to provide to data produced in the model. These values are only used to fill in type values where the dependent variable is missing. The first value is given to missing observations that precede the first observation, the second to those after the last observation, and the third for those following the final observation.
source_col	Column name containing source information for the data frame. If provided, the argument in source is used to fill in where predictions have filled in missing data.
source	Source to add to missing values.
scenario_detail_col	Column name containing scenario_detail information for the data frame. If provided, the argument in scenario_detail is used to fill in where prediction shave filled in missing data.
scenario_detail	Scenario details to add to missing values (usually the name of the model being used to generate the projection, optionally with relevant parameters).
replace_obs	Character value specifying how, if at all, observations should be replaced by fitted values. Defaults to replacing only missing values, but can be used to replace all values or none.

Value

Depending on the value passed to ret, either a data frame with predicted data, a vector of errors from model_error(), a fitted model, or a list with all 3.

caldwellst/augury documentation built on Oct. 10, 2024, 8:20 a.m.