Reference

uciwweihr_model_params1

Struct for holding parameters used in the UCIWWEIHR ODE compartmental model. Use create_uciwweihr_model_params to create an instance of this struct. With hard coded sigmawastewater and sigmahosp.

Fields

• E_init_sd::Float64=50.0: Standard deviation for the initial number of exposed individuals.
• log_E_init_mean::Int64=200: Mean for the initial number of exposed individuals, on log scale.
• I_init_sd::Float64=20.0: Standard deviation for the initial number of infected individuals.
• log_I_init_mean::Int64=100: Mean for the initial number of infected individuals, on log scale.
• H_init_sd::Float64=5.0: Standard deviation for the initial number of hospitalized individuals.
• log_H_init_mean::Int64=20: Mean for the initial number of hospitalized individuals, on log scale.
• gamma_sd::Float64=0.02: Standard deviation for the rate of incubation.
• log_gamma_mean::Float64=log(1/4): Mean for the rate of incubation on log scale.
• nu_sd::Float64=0.02: Standard deviation for the rate of leaving the infected compartment.
• log_nu_mean::Float64=log(1/7): Mean for the rate of leaving the infected compartment on the log scale.
• epsilon_sd::Float64=0.02: Standard deviation for the rate of hospitalization recovery.
• log_epsilon_mean::Float64=log(1/5): Mean for the rate of hospitalization recovery on the log scale.
• rho_gene_sd::Float64=0.02: Standard deviation for the rho prior.
• log_rho_gene_mean::Float64=log(0.011): Mean for the row prior on log scale.
• sigma_ww::Float64=log(0.1): Standard deviation for the normal distribution for wastewater data. Not infered.
• sigma_hosp::Float64=500.0: Standard deviation for the negative binomial distribution for hospital data. Not infered.
• Rt_init_sd::Float64=0.3: Standard deviation for the initial value of the time-varying reproduction number.
• Rt_init_mean::Float64=0.2: Mean for the initial value of the time-varying reproduction number.
• sigma_Rt_sd::Float64=0.2: Standard deviation for normal prior of log time-varying reproduction number standard deviation.
• sigma_Rt_mean::Float64=-3.0: Mean for normal prior of log time-varying reproduction number standard deviation.
• w_init_sd::Float64=0.1: Standard deviation for the initial value of the time-varying hospitalization rate.
• w_init_mean::Float64=log(0.35): Mean for the initial value of the time-varying hospitalization rate.
• sigma_w_sd::Float64=0.2: Standard deviation for normal prior of log time-varying hospitalization rate standard deviation.
• sigma_w_mean::Float64=-3.5: Mean for normal prior of time-varying hospitalization rate standard deviation.

uciwweihr_model_params2

Struct for holding parameters used in the UCIWWEIHR ODE compartmental model. Use create_uciwweihr_model_params to create an instance of this struct. With prior on sigmawastewater and sigmahosp.

Fields

• E_init_sd::Float64=50.0: Standard deviation for the initial number of exposed individuals.
• log_E_init_mean::Int64=200: Mean for the initial number of exposed individuals, on log scale.
• I_init_sd::Float64=20.0: Standard deviation for the initial number of infected individuals.
• log_I_init_mean::Int64=100: Mean for the initial number of infected individuals, on log scale.
• H_init_sd::Float64=5.0: Standard deviation for the initial number of hospitalized individuals.
• log_H_init_mean::Int64=20: Mean for the initial number of hospitalized individuals, on log scale.
• gamma_sd::Float64=0.02: Standard deviation for the rate of incubation.
• log_gamma_mean::Float64=log(1/4): Mean for the rate of incubation on log scale.
• nu_sd::Float64=0.02: Standard deviation for the rate of leaving the infected compartment.
• log_nu_mean::Float64=log(1/7): Mean for the rate of leaving the infected compartment on the log scale.
• epsilon_sd::Float64=0.02: Standard deviation for the rate of hospitalization recovery.
• log_epsilon_mean::Float64=log(1/5): Mean for the rate of hospitalization recovery on the log scale.
• rho_gene_sd::Float64=0.02: Standard deviation for the rho prior.
• log_rho_gene_mean::Float64=log(0.011): Mean for the row prior on log scale.
• sigma_ww_sd::Float64=nothing: Standard deviation for the normal prior of the log standard deviation of the wastewater data. If nothing, the sigma_ww is used.
• log_sigma_ww_mean::Float64=nothing: Mean for the normal prior of the log standard deviation of the wastewater data. If nothing, the sigma_ww is used.
• sigma_hosp_sd::Float64=nothing: Standard deviation for the normal prior of the log standard deviation of the hospital data. If nothing, the sigma_hosp is used.
• sigma_hosp_mean::Float64=nothing: Mean for the normal prior of the log standard deviation of the hospital data. If nothing, the sigma_hosp is used.
• Rt_init_sd::Float64=0.3: Standard deviation for the initial value of the time-varying reproduction number.
• Rt_init_mean::Float64=0.2: Mean for the initial value of the time-varying reproduction number.
• sigma_Rt_sd::Float64=0.2: Standard deviation for normal prior of log time-varying reproduction number standard deviation.
• sigma_Rt_mean::Float64=-3.0: Mean for normal prior of log time-varying reproduction number standard deviation.
• w_init_sd::Float64=0.1: Standard deviation for the initial value of the time-varying hospitalization rate.
• w_init_mean::Float64=log(0.35): Mean for the initial value of the time-varying hospitalization rate.
• sigma_w_sd::Float64=0.2: Standard deviation for normal prior of log time-varying hospitalization rate standard deviation.
• sigma_w_mean::Float64=-3.5: Mean for normal prior of time-varying hospitalization rate standard deviation.

uciwweihr_sim_params

Struct for holding parameters used in the UCIWWEIHR ODE compartmental model simulation. Use create_uciwweihr_sim_params to create an instance of this struct.

Fields

• time_points::Int64: Number of time points for the simulation.
• seed::Int64: Seed for random number generation.
• E_init::Int64: Initial number of exposed individuals.
• I_init::Int64: Initial number of infected individuals.
• H_init::Int64: Initial number of hospitalized individuals.
• gamma::Float64: Rate of incubation.
• nu::Float64: Rate of leaving the infected compartment.
• epsilon::Float64: Rate of hospitalization recovery.
• rho_gene::Float64: Contribution of infected individual's pathogen genome into wastewater.
• sigma_ww::Float64: standard deviation of the log concentration of pathogen genome in wastewater.
• sigma_hosp::Float64: Standard deviation for the negative binomial distribution for hospital data.
• Rt::Union{Float64, Vector{Float64}}: Initial value or time series of the time-varying reproduction number.
• sigma_Rt::Float64: Standard deviation for random walk of time-varying reproduction number.
• w::Union{Float64, Vector{Float64}}: Initial value or time series of the time-varying hospitalization rate.
• sigma_w::Float64: Standard deviation for random walk of time-varying hospitalization rate.
• rt_init::Float64: Initial value of the time-varying reproduction number, NOT USER SPECIFIED create_uciwweihr_params TAKES CARE OF THIS.
• w_init::Float64: Initial value of the time-varying hospitalization rate, NOT USER SPECIFIED create_uciwweihr_params TAKES CARE OF THIS.

ChainsCustomIndex(c::Chains, indices_to_keep::BitMatrix)

Reduce Chains object to only wanted indices.

Function created by Damon Bayer.

Create a re-parametrized negative binomial distribution in terms of mean and overdispersion.

Arguments

• μ: Mean of the distribution.
• ϕ: Overdispersion parameter.

Returns

A Distributions.NegativeBinomial distribution object.

calculate_quantiles(df, chain, var_prefix, quantiles)

Calculate quantiles for a given chain and variable prefix. Quantiles can be any user desired quantile.

calculate_quantiles_without_chain(df, var_prefix, quantiles)

Calculates quantiles for a given variable prefix without considering the chain.

create_uciwweihr_model_params1(; kwargs...) or create_uciwweihr_model_params2(; kwargs...)

Creates a uciwweihr_sim_params1 or uciwweihr_sim_params2 struct with the option to either hard code sigmawastewater and sigmahosp or provide a prior.

Arguments

• kwargs...: Named arguments corresponding to the fields in uciwweihr_sim_params1 or uciwweihr_sim_params2.

Returns

• params::uciwweihr_sim_params1 or params::uciwweihr_sim_params2: A struct with simulation parameters.

createuciwweihrsim_params(; kwargs...)

Creates a uciwweihr_sim_params struct with the option to either use a predetermined Rt and w or generate them as random walks.

Arguments

• kwargs...: Named arguments corresponding to the fields in uciwweihr_sim_params.

Returns

• params::uciwweihr_sim_params: A struct with simulation parameters.

eihr_ode!(du, u, p, t)

Calculate the ordinary differential equations (ODEs) for the EIHR model.

Parameters:

• du: Array{Float64,1} - The derivative of the state variables.
• u: Array{Float64,1} - The current state variables.
• p: Tuple{Float64,Float64,Float64,Float64,Float64} - The model parameters (alpha, gamma, nu, w, epsilon).
• t: Float64 - The current time.

Returns:

• du: Array{Float64,1} - The derivative of the state variables.

generate_ribbon_colors(number_of_colors)

Generates a vector with colors for ribbons in plots.

generate_logit_normal_random_walk(time_points::Int64, sigma::Float64, init_val::Float64)

Generates a logit-normal random walk time series.

Arguments

• time_points::Int64: Number of time points.
• sigma::Float64: Standard deviation of the random walk in logit space.
• init_val::Float64: Initial value of the random walk on the probability scale.

Returns

• walk::Vector{Float64}: Generated random walk on the probability scale.

generate_random_walk(time_points::Int64, sigma::Float64, init_val::Float64)

Generates a random walk time series.

Arguments

• time_points::Int64: Number of time points.
• sigma::Float64: Standard deviation of the random walk.
• init_val::Float64: Initial value of the random walk.

Returns

• walk::Vector{Float64}: Generated random walk.

Generating Simulation Data for Agent Based Model

To generate simulation data using the agent based model, you can use the generate_simulation_data_agent function defined in the UCIWWEIHR.jl package. This function allows you to customize various parameters for the simulation. NOT FINISHED, STILL NEEDS WW AND RT

Function Signature

Arguments

• seed::Int64: Seed for random number generation. Default value is 1.
• pop_size::Int64: Size of the population. Default value is 1000.
• I_init::Int64: Initial number of infected individuals. Default value is 200.
• H_init::Int64: Initial number of hospitalized individuals. Default value is 20.
• beta::Float64: Transmission rate. Default value is 0.001.
• gamma::Float64: Rate of exposed individuals becoming infectious. Default value is 1/4.
• nu::Float64: Rate of infected individuals recovering or getting hospitalized. Default value is 1/7.
• epsilon::Float64: Rate of hospitalized individuals recovering. Default value is 1/5.
• w_init::Float64: Probability of an infected individual becoming hospitalized. Default value is 0.35.

Returns

• df::DataFrame: A DataFrame containing the simulation data with columns Time, S, E, I, H, and R.

generate_simulation_data(params::UCIWWEIHRParams)

Generates simulation data for the UCIWWEIHR ODE compartmental model.

Arguments

• params::uciwweihr_sim_params: Struct containing parameters for the simulation.

Returns

• df::DataFrame: A DataFrame containing the simulation data with columns obstimes, log_ww_conc, hosp, rt, and wt.

is_time_varying_above_n(name, n)

Checks if the time varying parameter is above a given time point.

mcmcdiags_vis(...)

Default visualizer for results of the UCIWWEIHR model, includes posterior/priors of generated quantities and posterior predictive samples for forecasting. Forecasting plots will have the observed data alongside.

Arguments

• gq_samples: Generated quantities samples from the posterior/prior distribution, index 2 in uciwweihrgqpp output.
• desired_params: A list of lists of parameters to visualize. Each list will be visualized in a separate plot. Default is [["Einit", "Iinit", "Hinit"], ["gamma", "nu", "epsilon"], ["rhogene", "tau"], ["sigma_hosp"]].
• actual_non_time_varying_vals::uciwweihr_sim_params: A uciwweihrsimparams object of actual non-time varying parameter values if user has access to them. Default is nothing.
• save_plots::Bool=false: A boolean to indicate if user wants to save the plots as pngs into a plots folder.
• plot_name_to_save: A string to indicate the name of the plot to save. Default is "mcmcdiagnosisplots".

non_time_varying_param_vis(...)

Used in the uciwweihr_visualizer to create visuals for non-time varying parameters.

Arguments

• build_params::uciwweihr_model_params: A struct of model parameters used to build gq_samples, used only if user desired priors next to posteriors.
• data_hosp: Hospitalization data, used only if user desired priors next to posteriors.
• data_wastewater: Wastewater data, if model does not use this do not specify this, if user desires priors next to plot (do not specify if you do not want prior plots).
• obstimes_hosp: An array of time points for hospital data, used only if user desired priors next to posteriors.
• obstimes_wastewater: An array of time points for wastewater data, used only if user desired priors next to posteriors.
• param_change_times: An array of time points where the parameters change, used only if user desired priors next to posteriors.
• seed: An integer to set the seed for reproducibility, used only if user desired priors next to posteriors.
• forecast: A boolean to indicate if user wants to forecast, used only if user desired priors next to posteriors.
• forecast_weeks: An integer to indicate the number of weeks to forecast, used only if user desired priors next to posteriors.
• gq_samples: Generated quantities samples from the posterior/prior distribution, index 2 in uciwweihrgqpp output.
• desired_params: A list of lists of parameters to visualize. Each list will be visualized in a separate plot. Default is any parameter not in this list : ["alphat", "wt", "rtvals", "loggenes_mean", "H"]
• actual_non_time_varying_vals::uciwweihr_sim_params: A uciwweihrsimparams object of actual non-time varying parameter values if user has access to them. Default is nothing.
• save_plots::Bool=false: A boolean to indicate if user wants to save the plots as pngs into a plots folder.
• plot_name_to_save: A string to indicate the name of the plot to save. Default is "mcmcnontimevaryingparameterplots".

ode_solution_vis(...)

Visualizer for ODE solutions produced by the UCIWWEIHR model. If actual ode solutions are provided, they will be included in the plots.

Arguments

• gq_samples: Generated quantities samples from the posterior/prior distribution, index 2 in uciwweihrgqpp output.
• actual_non_time_varying_vals::uciwweihr_sim_params: A uciwweihrsimparams object of actual non-time varying parameter values if user has access to them. Default is nothing.
• save_plots::Bool=false: A boolean to indicate if user wants to save the plots as pngs into a plots folder.
• plot_name_to_save: A string to indicate the name of the plot to save. Default is "mcmcdiagnosisplots".

optimize_many_MAP(model, n_reps = 100, top_n = 1, verbose = true)

Try n_reps different initializations to get MAP estimate.

Function by Damon Bayer

optimize_many_MAP2(model, n_reps = 100, top_n = 1, verbose = true)

Try n_reps different initializations to get MAP estimate.

Modified by Christian Bernal Zelaya

optimize_many_MAP2_wrapper(...)

Wrapper function for optimizemanyMAP2 that uses model based on inputs to function.

Created by Christian Bernal Zelaya

power(a,b)

Raise `a` to the `b` power

predictive_param_vis(...)

Used in the uciwweihr_visualizer to create visuals for wastewater data and hospitalization data.

Arguments

• pp_samples: A DataFrame of posterior or prior predictive samples.
• data_wastewater: An array of actual wastewater values if user has access to them assumed, using time scale of observed time points. Default is nothing.
• data_hosp: An array of actual hospitalization values if user has access to them assumed, , using time scale of observed time points. Default is nothing.
• forecast_weeks: An integer of the number of weeks forecasted. Default is 0.
• vars_to_pred: A list of variables to predict. Default is ["datawastewater", "datahosp"].
• quantiles: A list of quantiles to calculate for ploting uncertainty. Default is [0.5, 0.8, 0.95].
• bayes_dist_type: A string to indicate if user is using Posterior or Prior distribution. Default is "Posterior".
• save_plots::Bool=false: A boolean to indicate if user wants to save the plots as pngs into a plots folder.
• plot_name_to_save: A string to indicate the name of the plot to save. Default is "mcmcpredparameter_plots".

repeat_last_n_elements(x::Vector{T}, n::Int, w::Int) where T

Modifies a given array so that the last n elements are repeated w times.

save_plots_to_docs(plot, filename; format = "png")

Saves plots to docs/plots directory.

startswith_any(name, patterns)

Checks if the name of time varying paramter starts with any of the patterns.

subset_desired_ode_from_gq(gq, desired_var::Regex)

Subsets the generated quantities dataframe from the uciwweihr_gq_pp output to only include the desired variable. desired_var should be put in the following form : r"I(\[|\])" for example.

uciwweihr_fit(...)

This is the sampler for the bayesian semi-parametric model for the wastewater EIHR compartmental model. The defaults for this fuction will follow those of the default simulation in generatesimulationdatawweihr.jl function.

Arguments

• data_hosp: An array of hospital data.
• data_wastewater: An array of pathogen genome concentration in localized wastewater data. If this is not avaliable, the model used will be one that only uses hospital data.
• obstimes: An array of timepoints for observed hosp/wastewater.
• priors_only::Bool=false: A boolean to indicate if only priors are to be sampled.
• n_samples::Int64=500: Number of samples to be drawn.
• n_chains::Int64=1: Number of chains to be run.
• n_discard_initial::Int64=0: Number of samples to be discarded.
• seed::Int64=2024: Seed for the random number generator.
• params::uciwweihr_model_params#: A struct containing parameters for the model. # is either 1 or 2 for prior or hardcoded sigmaww and sigmahosp.
• init_params: Initial parameters for the model. If supplied, uciwweihr_init_param can supply these values.
• return_bool: A boolean to indicate if the model is to use the return statement. Only set to false if only forecast is desired

Returns

• Samples from the posterior or prior distribution.

uciwweihr_gq_pp(...)

This generates quantities and a posterior predictive distribution for the bayesian semi-parametric model for the wastewater EIHR compartmental model. The defaults for this fuction will follow those of the default simulation in generatesimulationdatawweihr.jl function.

Arguments

• samples: Samples from the posterior/prior distribution.
• data_hosp: An array of hospital data.
• data_wastewater: An array of pathogen genome concentration in localized wastewater data. If this is not avaliable, the model used will be one that only uses hospital data.
• obstimes: An array of timepoints for observed hosp/wastewater.
• param_change_times: An array of timepoints where the parameters change.
• seed::Int64=2024: Seed for the random number generator.
• params::uciwweihr_model_params: A struct containing parameters for the model.
• forecast::Bool=false: A boolean to indicate if forecasting is to be done.
• forecast_weeks::Int64=4: Number of weeks to forecast.
• return_bool::Bool=true: A boolean to indicate if the model is to use the return statement. Only set to false if only forecast is desired
• gq_bool::Bool=true: A boolean to indicate if the model is to generate quantities. Only set to false if only forecast is desired

Returns

• Samples from the posterior or prior distribution.

uciwweihr_init_param(...)

Gets initial parameters values for the UCIWWEIHR model. Only need to run once.

Arguments

• data_hosp: An array of hospital data.
• data_wastewater: An array of pathogen genome concentration in localized wastewater data. If this is not avaliable, the model used will be one that only uses hospital data.
• obstimes_hosp: An array of timepoints for observed hosp.
• obstimes_wastewater: An array of timepoints for observed wastewater.
• param_change_times: An array of timepoints where the parameters change.
• n_chains::Int64=1: Number of chains to be run.
• seed::Int64=2024: Seed for the random number generator.
• params::uciwweihr_model_params: A struct containing parameters for the model.
• verbose_optimize::Bool=false: A boolean to indicate if the optimization process is to be verbose.

Returns

• Samples from the posterior or prior distribution.

uciwweihr_likelihood_helpers(...)
asdf

Arguments

• asdf

uciwweihr_model(...)

This is the bayesian semi-parametric model for the wastewater EIHR compartmental model. The defaults for this fuction will follow those of the default simulation in generatesimulationdatawweihr.jl function.

Arguments

• data_hosp: An array of hospital data.
• data_wastewater: An array of pathogen genome concentration in localized wastewater data. If this is not avaliable, the model used will be one that only uses hospital data.
• obstimes_hosp: An array of timepoints for observed hospital data.
• obstimes_wastewater: An array of timepoints for observed wastewater data.
• param_change_times: An array of timepoints where the parameters change.
• params::uciwweihr_model_params: A struct containing parameters for the model.
• return_bool: A boolean to indicate if the model is to use the return statement. Only set to false if only forecast is desired

uciwweihr_visualizer(...)

Default visualizer for results of the UCIWWEIHR model, includes posterior/priors of generated quantities and posterior predictive samples for forecasting. Forecasting plots will have the observed data alongside.

Arguments

• build_params::uciwweihr_model_params: A uciwweihrmodelparams object, if user desires priors next to plot (do not specify if you do not want prior plots).
• data_hosp: An array of hospital data used for model fitting, if user desires priors next to plot (do not specify if you do not want prior plots).
• data_wastewater: An array of wastewater data used for model fitting, if model does not use this do not specify this, if user desires priors next to plot (do not specify if you do not want prior plots).
• obstimes: An array of timepoints for observed hosp/wastewater, if user desires priors next to plot (do not specify if you do not want prior plots).
• param_change_times: An array of timepoints where the parameters change, if user desires priors next to plot (do not specify if you do not want prior plots).
• seed: Seed for the random number generator, if user desires priors next to plot (do not specify if you do not want prior plots).
• forecast: A boolean to indicate if forecasting is to be done, if user desires priors next to plot (do not specify if you do not want prior plots).
• forecast_weeks: Number of weeks to forecast, if user desires priors next to plot (do not specify if you do not want prior plots).
• pp_sampeles: Posterior predictive samples from the posterior/prior distribution, index 1 in uciwweihrgqpp output.
• gq_samples: Generated quantities samples from the posterior/prior distribution, index 2 in uciwweihrgqpp output.
• obs_data_hosp: An array of hospital data, data used for model fitting or extened timeseries for evaluation of forecast.
• obs_data_wastewater: An array of wastewater data, data used for model fitting or extened timeseries for evaluation of forecast.
• actual_rt_vals: An array of actual Rt values if user has access to them assumed to be on a daily scale. This typically will come from some simulation. Default is nothing.
• actual_w_t: An array of actual w_t values if user has access to them assumed to be on a daily scale. This typically will come from some simulation. Default is nothing.
• actual_E_ode_sol: An array of actual E ODE values if user has access to them assumed to be on a daily scale. This typically will come from some simulation. Default is nothing.
• actual_I_ode_sol: An array of actual I ODE values if user has access to them assumed to be on a daily scale. This typically will come from some simulation. Default is nothing.
• actual_H_ode_sol: An array of actual H ODE values if user has access to them assumed to be on a daily scale. This typically will come from some simulation. Default is nothing.
• actual_non_time_varying_vals::uciwweihr_sim_params: A uciwweihrsimparams object of actual non-time varying parameter values if user has access to them. Default is nothing.
• desired_params: A list of lists of parameters to visualize. Each list will be visualized in a separate plot. Default is [["Einit", "Iinit", "Hinit"], ["gamma", "nu", "epsilon"], ["rhogene", "tau", "df"], ["sigma_hosp"]].
• time_varying_params: A list of time varying parameters to visualize. Default is ["rtvals", "wt"].
• var_to_pred: A list of variables to predict. Default is ["datawastewater", "datahosp"].
• quantiles: A list of quantiles to calculate for ploting uncertainty. Default is [0.5, 0.8, 0.95].
• bayes_dist_type: A string to indicate if user is using Posterior or Prior distribution ("Posterior" / "Prior").
• mcmcdaigs::Bool=true: A boolean to indicate if user wants to visualize mcmc diagnosis plots and Effective Sample Size(ESS).
• time_varying_plots::Bool=true: A boolean to indicate if user wants to visualize time varying parameters.
• non_time_varying_plots::Bool=true: A boolean to indicate if user wants to visualize non-time varying parameters.
• ode_sol_plots::Bool=true: A boolean to indicate if user wants to visualize ODE solutions.
• pred_param_plots::Bool=true: A boolean to indicate if user wants to visualize posterior (or prior) predictive parameter results.
• save_plots::Bool=false: A boolean to indicate if user wants to save the plots as pngs into a plots folder.
• plot_name_to_save_mcmcdiag: A string to indicate the name of the plot to save for MCMC diagnostics. Default is "mcmcdiagnosisplots".
• plot_name_to_save_time_varying: A string to indicate the name of the plot to save for time varying parameters. Default is "mcmctimevaryingparameterplots".
• plot_name_to_save_non_time_varying: A string to indicate the name of the plot to save for non-time varying parameters. Default is "mcmcnontimevaryingparameterplots".
• plot_name_to_save_ode_sol: A string to indicate the name of the plot to save for ODE solutions. Default is "mcmcodesolution_plots".
• plot_name_to_save_pred_param: A string to indicate the name of the plot to save for posterior (or prior) predictive parameter results. Default is "mcmcpredparameter_plots".