Function reference
-
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 either in theformula_vars
oraverage_cols
, otherwise produce an error.
-
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...()
functions.
-
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 trend functions
-
assert_string()
- Assert that
x
is a character vector of lengthn
-
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 data
-
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 data
-
fit_forecast_model()
- Fit forecast model to data
-
fit_general_average_model()
- Fit general model to averages and apply trend to original data
-
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 modelling.
-
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
-
predict_aarr()
- Use annual average rate of reduction (AARR) to predict prevalence
-
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 and apply to original data
-
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 and apply to original data
-
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 apply to original data
-
predict_glmer()
- Use a generalized linear mixed-effects model to infill and project data
-
predict_glmer_avg_trend()
- Use
predict_glmer
on groups to generate average trend and apply to original data
-
predict_holt()
- Use Holt's linear trend exponential smoothing to forecast data
-
predict_holt_avg_trend()
- Use
predict_holt
on groups to generate average trend and apply to original data
-
predict_inla()
- Use Bayesian analysis of additive models to infill and project data
-
predict_inla_avg_trend()
- Use
predict_inla
on groups to generate average trend and apply to original data
-
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 apply to original data
-
predict_lme4()
- Use mixed models to infill and project data
-
predict_lme4_avg_trend()
- Use
predict_lme4
on groups to generate average trend and apply to original data
-
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 apply to original data
-
predict_nlmer()
- Use a non-linear mixed-effects model to infill and project data
-
predict_nlmer_avg_trend()
- Use
predict_nlmer
on groups to generate average trend and apply to original data
-
predict_ses()
- Use simple exponential smoothing to forecast data
-
predict_ses_avg_trend()
- Use
predict_ses
on groups to generate average trend and apply to original data
-
predict_simple()
- Use linear interpolation and flat extrapolation to infill data
-
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 average trend
-
trim_series()
- Get latest data for forecasting