predict_simple_fn: Linearly interpolate data

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

Linearly interpolate data

Description

predict_simple_fn() does simple linear interpolation and flat extrapolation on specified columnn in a data frame using zoo::na.approx().

Usage

predict_simple_fn(
  df,
  model,
  col,
  test_col = NULL,
  group_col = NULL,
  obs_filter = NULL,
  pred_col = "pred",
  sort_col = NULL,
  sort_descending = FALSE
)

Arguments

df	Data frame of model data.
model	Type of simple extrapolation or interpolation to perform: forward: Just flat_extrap and linear_interp. (default) all: All of flat_extrap, linear_interp, and back_extrap flat_extrap: Flat extrapolation from latest observed point. linear_interp: Linear interpolation between observed data points. back_extrap: Flat extrapolation from first observed data point backwards. both_extrap: Both flat_extrap and back_extrap.
col	Name of column to extrapolate/interpolate.
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".
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()`.
pred_col	Column name to store predicted value.
sort_col	Column name(s) to use to dplyr::arrange() the data prior to supplying type and calculating mean absolute scaled error on data involving time series. If NULL, not used. Defaults to "year". For predict_simple(), the first value in sort_col is passed to zoo::na.approx() as xout to ensure linear interpolation is based on sort_col indexing rather than default data frame indexing.
sort_descending	Logical value on whether the sorted values from sort_col should be sorted in descending order. Defaults to FALSE.

Value

A data frame.

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