Man pages for caldwellst/augury
Provides Streamlined Methods for Data Imputation and Forecasting for WHO DDI Statistics
| assert_columns | Assert that args in ellipses are columns in 'df' |
| assert_columns_unique | Assert that column names are not identical |
| assert_df | Assert that 'df' is a data frame |
| assert_error_correct_avg_trend | If using 'error_correct', then check that the columns are... |
| assert_function | Assert that 'x' is a function |
| assert_group_models | Assert if group_models TRUE then group_col not NULL |
| assert_group_sort_col | Assert sort column for use in average trend functions |
| assert_h | Assert that h, for forecasting, is > 0 |
| assert_inla | Assert if INLA is installed, for use in 'predict_inla...()'... |
| assert_model | Assert that 'x' is a function |
| assert_numeric | Assert numeric value |
| assert_numeric_cols | Assert columns in 'df' are numeric |
| assert_numeric_cols_avg | Assert columns in 'df' are numeric, for use with average... |
| assert_string | Assert that 'x' is a character vector of length 'n' |
| assert_test_col | Assert that test_col is of logical type |
| augury_add_columns | Adds empty columns to df |
| calculate_aarr | Extract AARR from vector of years and prevalence |
| calculate_sq_ch | Calculate change error |
| covariates_df | Default covariates for use in augury functions. |
| error_correct_fn | Use mean error to correct predictions |
| expand_df | Expand input data to make explicit missing values |
| expand_df_filter | Filter 'expand_df' |
| expand_df_min | Helper for 'expand_df_filter()' to calculate min for keeping... |
| filter_model_data | Filters data for modeling |
| fit_aarr_model | Generate prediction from model object |
| fit_forecast_average_model | Fit forecast model to averages and apply trend to original... |
| fit_forecast_model | Fit forecast model to data |
| fit_general_average_model | Fit general model to averages and apply trend to original... |
| fit_general_model | Fit general model to data |
| fit_inla_average_model | Fit INLA model to averages and apply trend to original data |
| fit_inla_model | Fit INLA model to data |
| fit_lme4_average_model | Fit mixed model to averages and apply trend to original data |
| fit_lme4_model | Fit general model to data |
| forecast_series | Forecast data series |
| get_average_df | Produces averaged data frame that can then be passed for... |
| get_forecast_data | Get data for forecast models |
| get_formula_avg_cols | Get variables that need to be averaged from formula. |
| get_model_data | Minimizes dataset to data needed for modelling |
| interpolate_aarr | Interpolate using AARR from vector of years and prevalence |
| join_covariates_df | Join data frame with covariates data frame |
| map_model_behavior | Catch instability of INLA |
| merge_average_df | Merge average df with predictions with original data frame |
| merge_prediction | Merge predicted data into data frame |
| model_error | Get modeling error from a data frame |
| parse_formula | Asserts formula and extract variables |
| parse_obs_filter | Parse obs filter intro string to be evaluated |
| pipe | Pipe operator |
| predict_aarr | Use annual average rate of reduction (AARR) to predict... |
| predict_average | Use averages to impute and forecast data |
| predict_average_fn | Impute data using simple averages |
| predict_forecast | Use a time series model to infill and project data |
| predict_forecast_avg_trend | Use 'predict_forecast' on groups to generate average trend... |
| predict_forecast_data | Generate prediction from model object |
| predict_general_data | Generate prediction from model object |
| predict_general_mdl | Use a generic R model to infill and project data |
| predict_general_mdl_avg_trend | Use 'predict_general_mdl' on groups to generate average trend... |
| predict_glm | Use a generalized linear model to infill and project data |
| predict_glm_avg_trend | Use 'predict_glm' on groups to generate average trend and... |
| predict_glmer | Use a generalized linear mixed-effects model to infill and... |
| predict_glmer_avg_trend | Use 'predict_glmer' on groups to generate average trend and... |
| predict_holt | Use Holt's linear trend exponential smoothing to forecast... |
| predict_holt_avg_trend | Use 'predict_holt' on groups to generate average trend and... |
| predict_inla | Use Bayesian analysis of additive models to infill and... |
| predict_inla_avg_trend | Use 'predict_inla' on groups to generate average trend and... |
| predict_inla_data | Generate prediction from an INLA output object |
| predict_inla_me | Use INLA for mixed effects modeling for prediction |
| predict_inla_ts | Use INLA for time series prediction |
| predict_lm | Use a linear model to infill and project data |
| predict_lm_avg_trend | Use 'predict_lm' on groups to generate average trend and... |
| predict_lme4 | Use mixed models to infill and project data |
| predict_lme4_avg_trend | Use 'predict_lme4' on groups to generate average trend and... |
| predict_lme4_data | Generate prediction from model object |
| predict_lmer | Use a linear mixed-effects model to infill and project data |
| predict_lmer_avg_trend | Use 'predict_lmer' on groups to generate average trend and... |
| predict_nlmer | Use a non-linear mixed-effects model to infill and project... |
| predict_nlmer_avg_trend | Use 'predict_nlmer' on groups to generate average trend and... |
| predict_ses | Use simple exponential smoothing to forecast data |
| predict_ses_avg_trend | Use 'predict_ses' on groups to generate average trend and... |
| predict_simple | Use linear interpolation and flat extrapolation to infill... |
| predict_simple_fn | Linearly interpolate data |
| probit_transform | Probit transform bounded data in a data frame |
| probit_vec | Probit transformation for bounded data |
| remove_groups | Remove groups from data frame if grouped |
| scale_transform | Scales data in a data frame |
| scale_vec | Scale a vector |
| simple_extrap | Helper function to do flat extrapolation |
| temp_fill | Fills vector backwards and forward, for use prior to applying... |
| trim_series | Get latest data for forecasting |
