View source: R/predict_lme4_avg_trend_wrappers.R

predict_lmer_avg_trend | R Documentation |

`predict_lmer`

on groups to generate average trend and apply to original data`predict_lmer_avg_trend()`

is a simple wrapper around `predict_lme4_avg_trend()`

.
For details surrounding the linear mixed effects model fitting, please see
lme4::lmer and for more details on the augury function this wraps around and the
various arguments this function accepts, please see `predict_lme4_avg_trend()`

.

```
predict_lmer_avg_trend(
df,
formula,
average_cols = NULL,
weight_col = NULL,
group_models = FALSE,
...,
ret = c("df", "all", "error", "model"),
scale = NULL,
probit = FALSE,
test_col = NULL,
test_period = NULL,
test_period_flex = NULL,
group_col = "iso3",
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("predictors", "response", "all", "none"),
type_col = NULL,
types = c("imputed", "imputed", "projected"),
source_col = NULL,
source = NULL,
scenario_detail_col = NULL,
scenario_detail = NULL,
replace_obs = c("missing", "all", "none"),
error_correct = FALSE,
error_correct_cols = NULL,
shift_trend = FALSE
)
```

`df` |
Data frame of model data. |

`formula` |
A formula that will be supplied to the model, such as |

`average_cols` |
Column name(s) of column(s) for use in grouping data for averaging, such as regions. If missing, uses global average of the data for infilling. |

`weight_col` |
Column name of column of weights to be used in averaging, such as country population. |

`group_models` |
Logical, if |

`...` |
Other arguments passed to the model 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 |

`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 |

`test_col` |
Name of logical column specifying which response values to remove
for testing the model's predictive accuracy. If |

`test_period` |
Length of period to test for RMChE. If |

`test_period_flex` |
Logical value indicating if |

`group_col` |
Column name(s) of group(s) to use in |

`obs_filter` |
String value of the form " 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 |

`sort_descending` |
Logical value on whether the sorted values from |

`pred_col` |
Column name to store predicted value. |

`pred_upper_col` |
Column name to store upper bound of confidence interval
generated by the |

`pred_lower_col` |
Column name to store lower bound of confidence interval
generated by the |

`upper_col` |
Column name that contains upper bound information, including
upper bound of the input data to the model. Values from |

`lower_col` |
Column name that contains lower bound information, including
lower bound of the input data to the model. Values from |

`filter_na` |
Character value specifying how, if at all, to filter |

`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` |
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` |
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. |

`error_correct` |
Logical value indicating whether or not whether mean error
should be used to adjust predicted values. If |

`error_correct_cols` |
Column names of data frame to group by when applying error correction to the predicted values. |

`shift_trend` |
Logical value specifying whether or not to shift predictions
so that the trend matches up to the last observation. If |

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.

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