Package index • ramp.work

Scaling

Compute Scaling Relationships

xds_scaling(): Scaling for Malaria Metrics

xds_scaling(<eir>): Compute eir-pr scaling relationships

xds_scaling(<Lambda>): Compute scaling relationships from mosquito emergence through PfPR

xds_scaling_Lambda(): Compute

compute_Lambda_threshold(): Get High/Low Values for Lambda

xds_plot_eirVpr(): xds_plot EIR(t) vs. the PR(t)

xds_plot_eirpr(): xds_plot the eir-pr scaling relationship

lines_eirVpr(): Add lines for the EIR(t) vs. the PR(t)

lines_eirpr(): Add lines for an eir-pr scaling relationship

Metrics

Metric Conversion

pr2Lambda(): Using the eirpr matrix and a RM xds_obj, convert pr to Lambda

xds_eir2ni(): Convert eir to ni

xds_eir2pr(): Convert eir to pr

xds_pr2eir(): Convert pr to eir

xds_pr2Lambda(): Convert pr to lambda

xds_pr2m(): Convert pr to mosquito density

xds_pr2ni(): Convert pr to ni

pr2history(): Reconstruct a history of exposure from a PR time series

pr2history_xm(): Reconstruct a history of exposure from a PR time series

pr2history_cbmc(): Reconstruct a history of exposure from a PR time series

eir2fqZ(): Convert the EIR into a vector describing infective biting density

fqZ2eir(): Convert a vector describing infective biting density into the EIR, $E$

make_HTC_matrix(): Parasite dispersal by humans

make_VC_matrix(): Parasite dispersal by mosquitoes

make_calR(): Parasite Dispersal through one Parasite Generation (Humans)

make_calZ(): Parasite Dispersal through one Parasite Generation (Mosquitoes)

Seasonality

No methods to set subclass

add_orbits(): Draw the orbit for the $i^{th}$ element of eirpr$scaling.

add_orbits_px(): Draw the orbit for the $i^{th}$ element of eirpr$scaling, and add points at the minimum and maximum eir and pr

compute_IoSD_F(): Compute the Index of Dispersal for a function F(t) over one year.

compute_IoSD_S(): Compute the Index of Seasonal Dispersal for a seasonal pattern $S(t)$ over one year. This assumes that $$\int_0^{365} S(t)dt = 365$$

compute_IoSD_pr(): Compute the Index of Seasonal Dispersal for a PR seasonal orbit for the $i^{th}$ element of eirpr$scaling

add_eirpr_orbits(): Draw the orbit for the $i^{th}$ element of eirpr$scaling.

Model Fitting

No methods to set subclass

setup_fitting(): Set up the xds_obj fitting object

update_fitting_ty(): feature Interpolation Points

fit_model(): Fit a model to data

scaling_init_ty(<Lambda>): Use scaling to set initial guesses

scaling_init_ty(): Use scaling to set initial guesses

Goodness of Fit

No methods to set subclass

compute_gof(): Compute GoF

compute_gof(<sse>): Compute GoF by LSS

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>): feature a function

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_coverage>): feature the irs coverage 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_coverage>): Setup indices for irs coverage

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_coverage>): Get initial X: IRS coverage

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

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_coverage>): 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_pre_obs_y(): Set up a hindcast for burn-in

setup_pre_obs_y(<use_first>): Set up the pre-observation interpolating points

setup_pre_obs_y(<mirror>): Set up the pre-observation interpolating points

setup_pre_obs_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_post_obs_y(): Forecast a Baseline

setup_post_obs_y(<asis>): Set forecast interpolation points

setup_post_obs_y(<use_last>): Set forecast interpolation points

Imputation

No methods to set subclass

setup_imputation(): Setup Imputation

get_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_ty(<all>): Get interpolation points

get_ty(<first>): Get interpolation points

get_ty(<ix>): Get the interpolation points

get_ty(<last>): Get interpolation points

get_ty(<nix>): Get interpolation points

get_ty(<tix>): Get interpolation points

get_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

setup_season(): Set up the seasonal pattern

setup_season(<list>): Set up the seasonal pattern

setup_season(<sin>): Set up the seasonal pattern

Fitting Utilities for Trends

Impute the baseline

setup_spline_fit(): Title

setup_trend_par(<Lambda>): Check the trend setup

setup_trend_par(): Check the trend setup

setup_trend_par(<eir>): Check the trend setup

Fitting Utilities for Coverage

Impute the baseline

fit_bednet_contact(): Fit bed net coverage

fit_irs_coverage(): Fit irs coverage

restore_pr2history(): Reconstruct a history of exposure from a PR time series

save_pr2history(): Save the history fitting

scaling_init_ty(<eir>): Use scaling to set initial guesses

change_forecast_ix(): Make a Forecast from Trusted Values

change_forecast_ty(): Impute the baseline

compute_impact(): Compute Measures of Impact

event_chop_spline_t(): Adjust Spacing

fit_shock(): Fit interannual variability using splines

fitting_change_spline_t(): Change Spline $t$ Values

fitting_change_spline_ty(): Change Spline $t,y$ Values

fitting_change_spline_y(): Change Spline $y$ Values

fitting_get_spline_ty(): Get Spline $t, y$ Values

fitting_replace_spline_t(): Replace Spline $t$ Values

fitting_replace_spline_ty(): Replace Spline $y$ Values

fitting_replace_spline_y(): Replace Spline $y$ Values

get_init_X(<shock>): Get Initial Values for Parameters

get_limits_X(<shock>): Get Initial Values for Parameters

get_yix_after_bednet_round(): Time Since Event

get_yix_after_irs_round(): Time Since Event

setup_fitting_indices(<shock>): Setup indices for

time_since_event(): Time Since Event

update_function_X(<shock>): Update the shock Function