Merge predicted data into data frame

Merges predicted data into data frame. By default, does not replace observed values with modeled data.

Usage

merge_prediction(
  df,
  response,
  group_col,
  obs_filter,
  sort_col,
  sort_descending,
  pred_col,
  pred_upper_col,
  pred_lower_col,
  upper_col,
  lower_col,
  type_col,
  types,
  source_col,
  source,
  scenario_detail_col,
  scenario_detail,
  replace_obs
)

Arguments

df

Data frame of model data.

response

Column name of response variable.

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()`.

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".

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).

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

A data frame.