Package index • ramp.work

Model Fitting

No methods to set subclass

setup_fitting(): Set up the xds_obj fitting object
setup_data(): Set up the data object for fitting
fit_model(): Fit a model to data
mask_data(): Mask data
unmask_data(): Unmask data
mask_events(): Mask data around events

Goodness of Fit

No methods to set subclass

compute_gof(): Compute GoF
compute_gof(<sse>): Compute SSE
compute_gof(<smooth_sse>): Compute SSE
smooth_pr(): Smooth PR
compute_gof_X(): Compute the GoF for X

Fit Part of a Model

No methods to set subclass

fit_mean_forcing(): Fit mean forcing
fit_season(): Fit a seasonal pattern
fit_season_amplitude(): Fit a seasonal pattern
fit_season_phase(): Fit a seasonal pattern
fit_season_bottom(): Fit a seasonal pattern
fit_season_pw(): Fit a seasonal pattern
fit_season_phase_alt(): Fit the phase
fit_trend(): Fit interannual variability using splines

Update Function

Update function with parameter values

update_function_X(): Update a function
update_function_X(<mean_forcing>): Update Mean Forcing
update_function_X(<phase>): feature a function
update_function_X(<bottom>): bottom
update_function_X(<pw>): feature a function
update_function_X(<season>): feature F_season
update_function_X(<amplitude>): feature a function
update_function_X(<trend>): Update the Trend Function
update_function_X(<irs_contact>): feature the irs contact function
update_function_X(<bednet_contact>): feature the bed net coverage function
update_function_X(<multifit>): Compute the GoF for X

Index Parameter Values

Index parameter values for fitting

setup_fitting_indices(): Set up indices for model fitting
setup_fitting_indices(<amplitude>): Seasonality: amplitude Indices
setup_fitting_indices(<bottom>): Seasonality: bottom Indices
setup_fitting_indices(<mean_forcing>): Indices for Mean Forcing
setup_fitting_indices(<phase>): Seasonality: phase Indices
setup_fitting_indices(<pw>): Seasonality: pw Indices
setup_fitting_indices(<season>): Seasonality Indices
setup_fitting_indices(<trend>): Setup indices for
setup_fitting_indices(<bednet_contact>): Setup indices for bednet coverage
setup_fitting_indices(<irs_contact>): Setup indices for irs contact

Modify Fitted Parameters Vector

Index parameter values for fitting

modify_vector_X(<NULL>): Replace Values in a List
modify_vector_X(): Replace
modify_vector_X(<numeric>): Replace Values in a List

Initial Guesses for Parameter Values

Set limits on parameter values for fitting

get_init_X(): Get Initial Values for Parameters
get_init_X(<amplitude>): Get Initial Values for Parameters
get_init_X(<bednet_contact>): Get initial X: Bed net coverage
get_init_X(<bottom>): Get Initial Values for Parameters
get_init_X(<irs_contact>): Get initial X: IRS contact
get_init_X(<mean_forcing>): Get Initial Values for Parameters
get_init_X(<multifit>): Compute the GoF for X
get_init_X(<phase>): Get Initial Values for Parameters
get_init_X(<pw>): Get Initial Values for Parameters
get_init_X(<season>): Get Initial Values for Parameters
get_init_X(<trend>): Get Initial Values for Parameters
sigX(): Sigmoid-X

Limits on Parameter Values

Set limits on parameter values for fitting

get_limits_X(): Get Limits
get_limits_X(<bednet_contact>): Get limits for IRS coverage parameters
get_limits_X(<bottom>): Get Initial Values for Parameters
get_limits_X(<irs_contact>): Get Initial Values for Parameters
get_limits_X(<mean_forcing>): Get Initial Values for Parameters
get_limits_X(<phase>): Get Initial Values for Parameters
get_limits_X(<pw>): Get Initial Values for Parameters
get_limits_X(<trend>): Get Initial Values for Parameters

Hindcasting

No methods to set subclass

setup_hindcast(): Hindcast a Baseline
hindcast_ty(): Hindcast a Baseline
setup_hindcast_y(): Set up a hindcast for burn-in
setup_hindcast_y(<use_first>): Set up the pre-observation interpolating points
setup_hindcast_y(<mirror>): Set up the pre-observation interpolating points
setup_hindcast_y(<asis>): Set up the pre-observation interpolating points

Forecasting

No methods to set subclass

forecast_ty(): Set the forecast interpolation
gam_forecast(): Fit and draw

Fitting

No methods to set subclass

show_fit(): Plot the model and the data
show_residuals(): Plot the model and the data
setup_forecast(): Set the forecast interpolation
setup_forecast_y(): Forecast a Baseline
setup_forecast_y(<asis>): Set forecast interpolation points
setup_forecast_y(<use_last>): Set forecast interpolation points
change_forecast_ix(): Make a Forecast from Trusted Values
change_forecast_ty(): Impute the baseline

Imputation

No methods to set subclass

setup_imputation(): Setup Imputation
get_trend_ty(): Get a set of trusted interpolation points
impute_spline_y(): Impute the baseline
impute_value(): Impute the baseline
impute_value(<first>): Use Mean for Modify Baseline
impute_value(<gam>): Baseline with gamma predictions
impute_value(<last>): Use Mean for Modify Baseline
impute_value(<max>): Use Max for Modify Baseline
impute_value(<mean>): Use Mean for Modify Baseline
impute_value(<median>): Use Median for Modify Baseline
impute_value(<min>): Use Min for Modify Baseline
impute_value(<reverse>): Use Mean for Modify Baseline
impute_value(<subsamp>): Create a modify Baseline
impute_value(<asis>): Use Mean for Modify Baseline

Get Spline Interpolation Points

No methods to set subclass

get_trend_ty(<all>): Get interpolation points
get_trend_ty(<first>): Get interpolation points
get_trend_ty(<ix>): Get the interpolation points
get_trend_ty(<last>): Get interpolation points
get_trend_ty(<nix>): Get interpolation points
get_trend_ty(<tix>): Get interpolation points
get_trend_ty(<unmodified>): Get the interpolation points

Fitting Utilities for Seasonality

Impute the baseline

preset_phase(): Initialize the phase parameter
approx_phase(): Compute the observed phase
check_season_par(<Lambda>): Check the seasonal setup
check_season_par(): Check the seasonal setup
check_season_par(<eir>): Check the seasonal setup
init_fit_season(): Set up the seasonal pattern
init_fit_season(<list>): Set up the seasonal pattern
init_fit_season(<sin>): Set up the seasonal pattern

Fitting Trends

Functions to fit trends

update_fit_trend(): feature Interpolation Points
get_fit_trend(): Get Spline \(t, y\) Values
compile_fit_trend(): Check the trend setup
compile_fit_trend(<Lambda>): Check the trend setup
compile_fit_trend(<eir>): Check the trend setup
crude_fit_trend(): Initialize the trend
crude_fit_trend(<Lambda>): Initialize the trend
crude_fit_trend(<eir>): Initialize the trend
change_all_fit_spline_ty(): Replace Spline \(y\) Values
change_all_fit_spline_y(): Replace Spline \(y\) Values
change_all_fit_spline_t(): Replace Spline \(t\) Values
change_ix_fit_spline_ty(): Change Spline \(t,y\) Values
change_ix_fit_spline_y(): Change Spline \(y\) Values
change_ix_fit_spline_t(): Change Spline \(t\) Values
add_fit_spline_ty(): Change Spline \(t,y\) Values
rm_ix_fit_spline_ty(): Change Spline \(t,y\) Values
event_chop_spline_t(): Adjust Spacing
get_yix_after_bednet_round(): Time Since Event
get_yix_after_irs_round(): Time Since Event
time_since_event(): Time Since Event

Fitting Vector Control

Impute the baseline

fit_bednet_contact(): Fit bed net coverage
fit_irs_contact(): Fit irs contact
restore_pr2history(): Reconstruct a history of exposure from a PR time series
save_pr2history(): Save the history fit_obj
compute_impact(): Compute Measures of Impact

IRS Shocks

Impute the baseline

fit_irs_shock(): Fit IRS Shock
get_init_X(<irs_shock>): Get initial X: IRS shock
setup_fitting_indices(<irs_shock>): Setup indices for irs shock
update_function_X(<irs_shock>): feature the irs shock function

Bed Net Shocks

Impute the baseline

fit_bednet_shock(): Fit Bednet Shock
get_init_X(<bednet_shock>): Get initial X: bednet shock
get_limits_X(<bednet_shock>): Get Initial Values for Parameters
setup_fitting_indices(<bednet_shock>): Setup indices for bednet shock
update_function_X(<bednet_shock>): feature the bednet shock function
fit_bednet_shock_d50(): Fit bednet d50
get_init_X(<bednet_shock_d50>): Get initial X: bednet d50
get_limits_X(<bednet_shock_d50>): Get Initial Values for Parameters
setup_fitting_indices(<bednet_shock_d50>): Setup indices for bednet d50
update_function_X(<bednet_shock_d50>): feature the bednet d50 function
X2dshape(): X2bottom
fit_bednet_dshape(): Fit bednet dshape
fit_bednet_shock_size(): Fit bednet shock_size
fit_irs_d50(): Fit IRS shock_d50
fit_irs_dshape(): IRS Effects: Fit Shape
fit_irs_shock_size(): Fit IRS shock_size
get_init_X(<bednet_dshape>): Get initial X: bednet dshape
get_init_X(<bednet_shock_size>): Get initial X: bednet shock_size
get_init_X(<irs_d50>): Get initial X: IRS shock_d50
get_init_X(<irs_dshape>): Get initial X: IRS dshape
get_init_X(<irs_shock_size>): Get initial X: IRS shock_size
get_limits_X(<bednet_dshape>): Get Initial Values for Parameters
get_limits_X(<bednet_shock_size>): Get Initial Values for Parameters
get_limits_X(<irs_d50>): Get Initial Values for Parameters
get_limits_X(<irs_dshape>): Get Initial Values for Parameters
get_limits_X(<irs_shock_size>): Get Initial Values for Parameters
setup_fitting_indices(<bednet_dshape>): Setup indices for bednet dshape
setup_fitting_indices(<bednet_shock_size>): Setup indices for bednet shock_size
update_function_X(<irs_dshape>): feature the irs dshape function
update_function_X(<irs_shock_size>): feature the irs shock_size function
setup_fitting_indices(<irs_d50>): Setup indices for irs shock_d50
setup_fitting_indices(<irs_dshape>): Setup indices for irs dshape
setup_fitting_indices(<irs_shock_size>): Setup indices for irs shock_size
update_function_X(<bednet_dshape>): feature the bednet dshape function
update_function_X(<bednet_shock_size>): feature the bednet shock_size function
update_function_X(<irs_d50>): feature the irs shock_d50 function
pr2shockfit(): Reconstruct a history of exposure from a PR time series
pr2shockfit_xm(): Reconstruct a history of exposure from a PR time series
restore_pr2shockit(): Reconstruct a history of exposure from a PR time series
save_pr2shockit(): Save the history fit_obj
sigXinv(): Sigmoid-X Inverse
norm_trend(): Normalize Trend
norm_trend(<eir>): Normalize Trend
norm_trend(<Lambda>): Normalize Trend

New

compute_gof(<ts_sse>): Compute SSE
mask_bn_event(): Mask data around a bednet event
pr2history(): Reconstruct a history of exposure from a PR time series
pr2history_xm(): Reconstruct a history of exposure from a PR time series
reconD_shock_xm(): Reconstruct a history of exposure from a PR time series
setup_fitting_ts(): Set up the ts_obj fitting object
ts_setup(): Build a Decomposable Time Series Model