calibR_R6: R6 Class representing a calibR_R6 calibration machine.
In W-Mohammed/calibrater: Calibration of Decision-Analytic Models
calibR_R6 R Documentation
R6 Class representing a calibR_R6 calibration machine.
Description
An instance of this class is expected to calibrate a user-defined model
and produce summary plots/tables.
Format
An R6::R6Class object.
Public fields
calibration_modela Decision-Analytic model under calibration
calibration_model_argsarguments passed to the calibration
model
calibration_parameterscalibration parameters' data
calibration_targetscalibration targets' data
calibration_resultscalibration interim results
model_interventionsmodel interventions to run cost-effectiveness
analysis
transform_parameterslogical for whether to back transform
parameters
GOF_measure_plotlog likelihood values for plot
model_predictionssimulated outputs
prior_samplessamples from parameters' priors
maximum_a_posterioriparameter set with maximum posterior
probability
PSA_samplescalibration and un-calibration parameters
samples
PSA_resultsPSA results
PSA_summaryPSA results' summary
PSA_summary_tablesPSA results' summary tables
simulated_targetsPSA results
plotssummary plots
Methods
Public methods
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Method new()
Sample prior distribution(s) using one or more sampling method.
This function currently supports: LHS, FGS and RGS.
Usage
calibR_R6$new(.model, .args, .params, .targets, .transform, .intervs = NULL)
Arguments
.modelThe decision analytic model under calibration
.argsCalibration model arguments
.paramsCalibration parameters
.targetsCalibration targets
.transformLogical for whether the model will use
transformed parameters. This allows some functions to back
transform the parameters to their original scale before using them.
.intervsA list containing the information about the considered
interventions built into the model.
Returns
Object of class CalibrateR_R6
Examples
\dontrun{
}
Method sampleR()
Sample prior distribution(s) using one or more sampling method.
This function currently supports: LHS, FGS and RGS.
Usage
calibR_R6$sampleR(.n_samples = 1, .sampling_method, .ssed_no = NULL)
Arguments
.n_samplesAn integer specifying the number of samples to be
generated.
.sampling_methodThe sampling method(s) to use. The function
currently supports LHS, RGS, and FGS.
.ssed_norandom seed number
Returns
Executes the required sampling method and populates the
"prior samples" internal object.
Examples
\dontrun{
}
Method calibrateR_random()
Calibrate the model using one or more random search method(s).
Usage
calibR_R6$calibrateR_random(
.optim = FALSE,
.maximise = TRUE,
.weighted = NULL,
.sample_method = "LHS",
.calibration_method = "LLK",
.calibration_function = NULL
)
Arguments
.optimLogical for whether the function is used by an
optimisation algorithm. Default is FALSE.
.maximiseLogical for whether the output of the function is
used in a maximising optimisation function. Default is TRUE.
.weightedLogical for whether the SSE was to be weighted,
default is TRUE. The weight used by function is
1/(sd^2).
.sample_methodThe method used to sample from the prior
distribution.
.calibration_methodgoodness-of-fit method name.
.calibration_functiongoodness-of-fit function.
Returns
Executes the required calibration method and populates
the samples internal object.
Examples
\dontrun{
}
Method calibrateR_directed()
Calibrate the model using one or more directed search method(s).
Usage
calibR_R6$calibrateR_directed(
.gof = "LLK",
.gof_func = NULL,
.n_samples = 1,
.max_iterations = 1000,
temp = 10,
trace = NULL,
.calibration_method = "NM",
.sample_method = "LHS",
.maximise = TRUE
)
Arguments
.gofName of goodness-of-fit function, default is
log-likelihood.
.gof_funcGoodness-of-fit function; if NULL (default) the
supported function defined by .gof will be used
.n_samplesNumber of Starting sets (gausses) to use.
.max_iterationsMaximum number of algorithm iterations.
tempSANN algorithm tuning parameter.
traceNon-negative integer. If positive, tracing information on
the progress of the optimization is produced. Higher values may produce
more tracing information.
.calibration_methodThe calibration process.
.sample_methodThe method used to sample from the prior
distribution.
.maximiseLogical for whether the function is to maximise.
Returns
Executes the required calibration method and populates
the samples internal object.
Examples
\dontrun{
}
Method calibrateR_bayesian()
Calibrate the model using one or more calibration method.
Usage
calibR_R6$calibrateR_bayesian(
.b_methods = "SIR",
.n_resample = 1000,
.IMIS_iterations = 10,
.IMIS_sample = 100,
.MCMC_burnIn = 10000,
.MCMC_samples = 50000,
.MCMC_thin = 5,
.MCMC_rerun = TRUE,
.diag_ = FALSE
)
Arguments
.b_methodsBayesian calibration method(s)
.n_resampleDesired number of draws from the posterior
.IMIS_iterationsMaximum number of IMIS iterations
.IMIS_samplePositive integer for the IMIS sample size at each
iteration.
.MCMC_burnInPositive integer for the MCMC burn-in sample.
.MCMC_samplesPositive integer for the total MCMC sample,
including burn-in.
.MCMC_thinPositive integer for the value by which MCMC results
are to reduced. .MCMC_thin defines the number of samples from which the
first will be retained while the rest are discarded.
.MCMC_rerunLogical for whether to re-run MCMC using the proposal
distribution covariance matrix from the first run.
.diag_Logical for whether to print diagnostics.
Returns
Executes the required calibration method and populates
the samples internal object.
Examples
\dontrun{
}
Method sample_PSA_values()
Sample PSA values for the calibration parameters
Usage
calibR_R6$sample_PSA_values(.calibration_methods, .PSA_samples)
Arguments
.calibration_methodsBayesian calibration method(s)
.PSA_samplesNumber of PSA sets to sample
Examples
\dontrun{
}
Method run_PSA()
Run PSA
Usage
calibR_R6$run_PSA(.PSA_unCalib_values_ = NULL)
Arguments
.PSA_unCalib_values_PSA values for un-calibrated parameters
Examples
\dontrun{
}
Method summarise_PSA()
Summarise PSA results
Usage
calibR_R6$summarise_PSA()
Examples
\dontrun{
}
Method draw_plots()
Draw plots
Usage
calibR_R6$draw_plots(
prior_sample_method = "LHS",
print_pair_correlations = FALSE
)
Arguments
prior_sample_methodSampling method used to generate prior
samples.
print_pair_correlationsPrint pair-wise correlations to
console.
Examples
\dontrun{
}
Method draw_GOF_measure()
Plot Goodness of fit function(s)
Usage
calibR_R6$draw_GOF_measure(
.engine_ = "plotly",
.blank_contour_ = TRUE,
.gof_ = "LLK",
.percent_sampled_ = 10,
.n_samples_ = 10000,
.true_points_ = FALSE,
.greys_ = FALSE,
.scale_ = NULL,
.coloring_ = "fill",
.legend_ = FALSE,
.zoom_ = FALSE,
.x_axis_lb_ = NULL,
.x_axis_ub_ = NULL,
.y_axis_lb_ = NULL,
.y_axis_ub_ = NULL,
.save_ = FALSE,
.saving_path_ = here::here(),
.saving_image_dir_ = "/images/GOFs/",
.saving_x_params_ = 1,
.saving_image_scale_ = 2,
.saving_image_width_ = 1000,
.saving_image_height_ = 600
)
Arguments
.engine_String naming the plotting engine, currently "plotly".
.blank_contour_Logical for whether to only plot blank or empty GOF
contour plots.
.gof_Goodness of fit (GOF) measure - fitness function. Either
"LLK" or "SEE" for the log-likelihood and sum-of-squared-errors GOF,
respectively.
.percent_sampled_.percent_sampled_ The fraction of LHS, RGS, or
FGS samples to select.
.n_samples_Number of Grid samples to plot log likelihood
.true_points_Logical for whether to add "True set" to plots.
.greys_Logical for whether to use a Grey scale in the plot.
.scale_The colour bar colour-scale. Available options are Greys,
YlGnBu, Greens, YlOrRd, Bluered, RdBu, Reds, Blues, Picnic, Rainbow,
Portland, Jet, Hot, Blackbody, Earth, Electric, Viridis, Cividis.
.coloring_Which contouring is required (default fill) and options
are "fill" | "heatmap" | "lines" | "none"
.legend_Logical for whether to show a legend (default is FALSE).
This parameter also controls text labels in the opposite way.
.zoom_Logical (default FALSE) for whether to limit the resulting
plot to the min() and max() of the two dimensional contour plots.
.x_axis_lb_Lower bound of the plot's x axis.
.x_axis_ub_Upper bound of the plot's x axis.
.y_axis_lb_Lower bound of the plot's y axis.
.y_axis_ub_Upper bound of the plot's y axis.
.save_Logical for whether to save plots.
.saving_path_String defining the path for where to save the
plots.
.saving_image_dir_String defining the sub-folder in the path
where to save the plots.
.saving_x_params_Integer or vector of integers for the rank(s) of
.saving_image_scale_Positive integer dictating the scale at which
the image is saved. Default value is 5 for images with width of 1/3 an A4
paper.
.saving_image_width_Positive integer setting the width of the
saved version of the plot. The default (1,000 px) is appropriate for an
image 1/3 of the width of A4 sheet.
.saving_image_height_Positive integer setting the height of the
saved version of the plot. The default (600 px) is appropriate for an
image 1/3 of the width of A4 sheet. #'
Examples
\dontrun{
}
Method draw_targets_plots()
Plot Targets (with or without simulated targets)
Usage
calibR_R6$draw_targets_plots(
.engine_ = "ggplot2",
.sim_targets_ = FALSE,
.calibration_methods_ = "all",
.legend_pos_ = "none",
.PSA_samples_ = NULL,
.PSA_unCalib_values_ = NULL,
.save_ = FALSE,
.saving_path_ = here::here(),
.saving_image_dir_ = "/images/Targets/",
.saving_image_scale_ = 2,
.saving_image_width_ = 1000,
.saving_image_height_ = 600,
.saving_image_units_ = "px"
)
Arguments
.engine_String naming the plotting engine, currently "ggplot2".
.sim_targets_Logical (default FALSE) for whether generate then
plot simulated targets. The generation of simulated targets requires
the package to run the model using the sampled or identified PSA values.
.calibration_methods_String vector naming the calibration methods
for which simulated targets are to be generated. Options is either "all"
(the default) or any of c("random", "directed", "bayesian").
.legend_pos_String declaring the preferred position for the
legend. Default is "bottom".
.PSA_samples_Integer defining the maximum number of PSA values to
use in plotting/generating the simulated targets.
.PSA_unCalib_values_Tibble/table/dataframe containing the PSA
draws for the parameters that are not calibrated in the object
.save_Logical for whether to save plots.
.saving_path_String defining the path for where to save the
plots.
.saving_image_dir_String defining the sub-folder in the path
where to save the plots.
.saving_image_scale_Positive integer dictating the scale at which
the image is saved. Default value is 5 for images with width of 1/3 an A4
paper.
.saving_image_width_Positive integer setting the width of the
saved version of the plot. The default (1,000 px) is appropriate for an
image 1/3 of the width of A4 sheet.
.saving_image_height_Positive integer setting the height of the
saved version of the plot. The default (600 px) is appropriate for an
image 1/3 of the width of A4 sheet.
.saving_image_units_A string (default "px") defining the units
in which the width and height are provided.
Examples
\dontrun{
}
Method draw_distributions_plots()
Plot prior and posterior distributions
Usage
calibR_R6$draw_distributions_plots(
.engine_ = "ggplot2",
.bins_ = 20,
.legend_pos_ = "none",
.log_scaled_ = FALSE,
.save_ = FALSE,
.saving_path_ = here::here(),
.saving_image_dir_ = "/images/Prior-posterior/",
.saving_image_scale_ = 2,
.saving_image_width_ = 1000,
.saving_image_height_ = 600,
.saving_image_units_ = "px"
)
Arguments
.engine_String naming plotting package currently only supports
"ggplot2".
.bins_Numeric specifying the number of bins in the histograms.
.legend_pos_String (default bottom) setting legend position.
.log_scaled_Logical for whether to use log scale in the x axis.
.save_Logical for whether to save plots.
.saving_path_String defining the path for where to save the
plots.
.saving_image_dir_String defining the sub-folder in the path
where to save the plots.
.saving_image_scale_Positive integer dictating the scale at which
the image is saved. Default value is 5 for images with width of 1/3 an A4
paper.
.saving_image_width_Positive integer setting the width of the
saved version of the plot. The default (1,000 px) is appropriate for an
image 1/3 of the width of A4 sheet.
.saving_image_height_Positive integer setting the height of the
saved version of the plot. The default (600 px) is appropriate for an
image 1/3 of the width of A4 sheet.
.saving_image_units_A string (default "px") defining the units
in which the width and height are provided.
Examples
\dontrun{
}
Method draw_PSA_summary_tables()
Draw PSA summary tables:—-
Usage
calibR_R6$draw_PSA_summary_tables(
.save_ = FALSE,
.saving_path_ = here::here(),
.saving_data_dir_ = "/data/PSA tables/"
)
Arguments
.save_Logical for whether to save tables data.
.saving_path_String defining the path for where to save the
tables data.
.saving_data_dir_String defining the sub-folder in the path
where to save the tables data.
Examples
\dontrun{
}
Method clone()
The objects of this class are cloneable with this method.
Usage
calibR_R6$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## Not run:
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## Method `calibR_R6$new`
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## Method `calibR_R6$sampleR`
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## Method `calibR_R6$calibrateR_random`
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## Method `calibR_R6$calibrateR_directed`
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## Method `calibR_R6$calibrateR_bayesian`
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## Method `calibR_R6$sample_PSA_values`
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## Method `calibR_R6$run_PSA`
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## Method `calibR_R6$summarise_PSA`
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## Method `calibR_R6$draw_plots`
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## Method `calibR_R6$draw_GOF_measure`
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## Method `calibR_R6$draw_targets_plots`
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## Method `calibR_R6$draw_distributions_plots`
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## Method `calibR_R6$draw_PSA_summary_tables`
## ------------------------------------------------
## Not run:
W-Mohammed/calibrater documentation built on Oct. 14, 2023, 1:57 a.m.
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| calibR_R6 | R Documentation |
R6 Class representing a calibR_R6 calibration machine.
Description
An instance of this class is expected to calibrate a user-defined model and produce summary plots/tables.
Format
An R6::R6Class object.
Public fields
calibration_modela Decision-Analytic model under calibration
calibration_model_argsarguments passed to the calibration model
calibration_parameterscalibration parameters' data
calibration_targetscalibration targets' data
calibration_resultscalibration interim results
model_interventionsmodel interventions to run cost-effectiveness analysis
transform_parameterslogical for whether to back transform parameters
GOF_measure_plotlog likelihood values for plot
model_predictionssimulated outputs
prior_samplessamples from parameters' priors
maximum_a_posterioriparameter set with maximum posterior probability
PSA_samplescalibration and un-calibration parameters samples
PSA_resultsPSA results
PSA_summaryPSA results' summary
PSA_summary_tablesPSA results' summary tables
simulated_targetsPSA results
plotssummary plots
Methods
Public methods
Method new()
Sample prior distribution(s) using one or more sampling method. This function currently supports: LHS, FGS and RGS.
Usage
calibR_R6$new(.model, .args, .params, .targets, .transform, .intervs = NULL)
Arguments
.modelThe decision analytic model under calibration
.argsCalibration model arguments
.paramsCalibration parameters
.targetsCalibration targets
.transformLogical for whether the model will use transformed parameters. This allows some functions to back transform the parameters to their original scale before using them.
.intervsA list containing the information about the considered interventions built into the model.
Returns
Object of class CalibrateR_R6
Examples
\dontrun{
}
Method sampleR()
Sample prior distribution(s) using one or more sampling method. This function currently supports: LHS, FGS and RGS.
Usage
calibR_R6$sampleR(.n_samples = 1, .sampling_method, .ssed_no = NULL)
Arguments
.n_samplesAn integer specifying the number of samples to be generated.
.sampling_methodThe sampling method(s) to use. The function currently supports LHS, RGS, and FGS.
.ssed_norandom seed number
Returns
Executes the required sampling method and populates the "prior samples" internal object.
Examples
\dontrun{
}
Method calibrateR_random()
Calibrate the model using one or more random search method(s).
Usage
calibR_R6$calibrateR_random( .optim = FALSE, .maximise = TRUE, .weighted = NULL, .sample_method = "LHS", .calibration_method = "LLK", .calibration_function = NULL )
Arguments
.optimLogical for whether the function is used by an optimisation algorithm. Default is
FALSE..maximiseLogical for whether the output of the function is used in a maximising optimisation function. Default is
TRUE..weightedLogical for whether the SSE was to be weighted, default is
TRUE. The weight used by function is1/(sd^2)..sample_methodThe method used to sample from the prior distribution.
.calibration_methodgoodness-of-fit method name.
.calibration_functiongoodness-of-fit function.
Returns
Executes the required calibration method and populates the samples internal object.
Examples
\dontrun{
}
Method calibrateR_directed()
Calibrate the model using one or more directed search method(s).
Usage
calibR_R6$calibrateR_directed( .gof = "LLK", .gof_func = NULL, .n_samples = 1, .max_iterations = 1000, temp = 10, trace = NULL, .calibration_method = "NM", .sample_method = "LHS", .maximise = TRUE )
Arguments
.gofName of goodness-of-fit function, default is log-likelihood.
.gof_funcGoodness-of-fit function; if NULL (default) the supported function defined by
.gofwill be used.n_samplesNumber of Starting sets (gausses) to use.
.max_iterationsMaximum number of algorithm iterations.
tempSANN algorithm tuning parameter.
traceNon-negative integer. If positive, tracing information on the progress of the optimization is produced. Higher values may produce more tracing information.
.calibration_methodThe calibration process.
.sample_methodThe method used to sample from the prior distribution.
.maximiseLogical for whether the function is to maximise.
Returns
Executes the required calibration method and populates the samples internal object.
Examples
\dontrun{
}
Method calibrateR_bayesian()
Calibrate the model using one or more calibration method.
Usage
calibR_R6$calibrateR_bayesian( .b_methods = "SIR", .n_resample = 1000, .IMIS_iterations = 10, .IMIS_sample = 100, .MCMC_burnIn = 10000, .MCMC_samples = 50000, .MCMC_thin = 5, .MCMC_rerun = TRUE, .diag_ = FALSE )
Arguments
.b_methodsBayesian calibration method(s)
.n_resampleDesired number of draws from the posterior
.IMIS_iterationsMaximum number of IMIS iterations
.IMIS_samplePositive integer for the IMIS sample size at each iteration.
.MCMC_burnInPositive integer for the MCMC burn-in sample.
.MCMC_samplesPositive integer for the total MCMC sample, including burn-in.
.MCMC_thinPositive integer for the value by which MCMC results are to reduced. .MCMC_thin defines the number of samples from which the first will be retained while the rest are discarded.
.MCMC_rerunLogical for whether to re-run MCMC using the proposal distribution covariance matrix from the first run.
.diag_Logical for whether to print diagnostics.
Returns
Executes the required calibration method and populates the samples internal object.
Examples
\dontrun{
}
Method sample_PSA_values()
Sample PSA values for the calibration parameters
Usage
calibR_R6$sample_PSA_values(.calibration_methods, .PSA_samples)
Arguments
.calibration_methodsBayesian calibration method(s)
.PSA_samplesNumber of PSA sets to sample
Examples
\dontrun{
}
Method run_PSA()
Run PSA
Usage
calibR_R6$run_PSA(.PSA_unCalib_values_ = NULL)
Arguments
.PSA_unCalib_values_PSA values for un-calibrated parameters
Examples
\dontrun{
}
Method summarise_PSA()
Summarise PSA results
Usage
calibR_R6$summarise_PSA()
Examples
\dontrun{
}
Method draw_plots()
Draw plots
Usage
calibR_R6$draw_plots( prior_sample_method = "LHS", print_pair_correlations = FALSE )
Arguments
prior_sample_methodSampling method used to generate prior samples.
print_pair_correlationsPrint pair-wise correlations to console.
Examples
\dontrun{
}
Method draw_GOF_measure()
Plot Goodness of fit function(s)
Usage
calibR_R6$draw_GOF_measure( .engine_ = "plotly", .blank_contour_ = TRUE, .gof_ = "LLK", .percent_sampled_ = 10, .n_samples_ = 10000, .true_points_ = FALSE, .greys_ = FALSE, .scale_ = NULL, .coloring_ = "fill", .legend_ = FALSE, .zoom_ = FALSE, .x_axis_lb_ = NULL, .x_axis_ub_ = NULL, .y_axis_lb_ = NULL, .y_axis_ub_ = NULL, .save_ = FALSE, .saving_path_ = here::here(), .saving_image_dir_ = "/images/GOFs/", .saving_x_params_ = 1, .saving_image_scale_ = 2, .saving_image_width_ = 1000, .saving_image_height_ = 600 )
Arguments
.engine_String naming the plotting engine, currently "plotly".
.blank_contour_Logical for whether to only plot blank or empty GOF contour plots.
.gof_Goodness of fit (GOF) measure - fitness function. Either "LLK" or "SEE" for the log-likelihood and sum-of-squared-errors GOF, respectively.
.percent_sampled_.percent_sampled_ The fraction of LHS, RGS, or FGS samples to select.
.n_samples_Number of Grid samples to plot log likelihood
.true_points_Logical for whether to add "True set" to plots.
.greys_Logical for whether to use a Grey scale in the plot.
.scale_The colour bar colour-scale. Available options are Greys, YlGnBu, Greens, YlOrRd, Bluered, RdBu, Reds, Blues, Picnic, Rainbow, Portland, Jet, Hot, Blackbody, Earth, Electric, Viridis, Cividis.
.coloring_Which contouring is required (default fill) and options are "fill" | "heatmap" | "lines" | "none"
.legend_Logical for whether to show a legend (default is FALSE). This parameter also controls text labels in the opposite way.
.zoom_Logical (default FALSE) for whether to limit the resulting plot to the min() and max() of the two dimensional contour plots.
.x_axis_lb_Lower bound of the plot's x axis.
.x_axis_ub_Upper bound of the plot's x axis.
.y_axis_lb_Lower bound of the plot's y axis.
.y_axis_ub_Upper bound of the plot's y axis.
.save_Logical for whether to save plots.
.saving_path_String defining the path for where to save the plots.
.saving_image_dir_String defining the sub-folder in the path where to save the plots.
.saving_x_params_Integer or vector of integers for the rank(s) of
.saving_image_scale_Positive integer dictating the scale at which the image is saved. Default value is 5 for images with width of 1/3 an A4 paper.
.saving_image_width_Positive integer setting the width of the saved version of the plot. The default (1,000 px) is appropriate for an image 1/3 of the width of A4 sheet.
.saving_image_height_Positive integer setting the height of the saved version of the plot. The default (600 px) is appropriate for an image 1/3 of the width of A4 sheet. #'
Examples
\dontrun{
}
Method draw_targets_plots()
Plot Targets (with or without simulated targets)
Usage
calibR_R6$draw_targets_plots( .engine_ = "ggplot2", .sim_targets_ = FALSE, .calibration_methods_ = "all", .legend_pos_ = "none", .PSA_samples_ = NULL, .PSA_unCalib_values_ = NULL, .save_ = FALSE, .saving_path_ = here::here(), .saving_image_dir_ = "/images/Targets/", .saving_image_scale_ = 2, .saving_image_width_ = 1000, .saving_image_height_ = 600, .saving_image_units_ = "px" )
Arguments
.engine_String naming the plotting engine, currently "ggplot2".
.sim_targets_Logical (default FALSE) for whether generate then plot simulated targets. The generation of simulated targets requires the package to run the model using the sampled or identified PSA values.
.calibration_methods_String vector naming the calibration methods for which simulated targets are to be generated. Options is either "all" (the default) or any of c("random", "directed", "bayesian").
.legend_pos_String declaring the preferred position for the legend. Default is "bottom".
.PSA_samples_Integer defining the maximum number of PSA values to use in plotting/generating the simulated targets.
.PSA_unCalib_values_Tibble/table/dataframe containing the PSA draws for the parameters that are not calibrated in the object
.save_Logical for whether to save plots.
.saving_path_String defining the path for where to save the plots.
.saving_image_dir_String defining the sub-folder in the path where to save the plots.
.saving_image_scale_Positive integer dictating the scale at which the image is saved. Default value is 5 for images with width of 1/3 an A4 paper.
.saving_image_width_Positive integer setting the width of the saved version of the plot. The default (1,000 px) is appropriate for an image 1/3 of the width of A4 sheet.
.saving_image_height_Positive integer setting the height of the saved version of the plot. The default (600 px) is appropriate for an image 1/3 of the width of A4 sheet.
.saving_image_units_A string (default "px") defining the units in which the width and height are provided.
Examples
\dontrun{
}
Method draw_distributions_plots()
Plot prior and posterior distributions
Usage
calibR_R6$draw_distributions_plots( .engine_ = "ggplot2", .bins_ = 20, .legend_pos_ = "none", .log_scaled_ = FALSE, .save_ = FALSE, .saving_path_ = here::here(), .saving_image_dir_ = "/images/Prior-posterior/", .saving_image_scale_ = 2, .saving_image_width_ = 1000, .saving_image_height_ = 600, .saving_image_units_ = "px" )
Arguments
.engine_String naming plotting package currently only supports "ggplot2".
.bins_Numeric specifying the number of bins in the histograms.
.legend_pos_String (default bottom) setting legend position.
.log_scaled_Logical for whether to use log scale in the x axis.
.save_Logical for whether to save plots.
.saving_path_String defining the path for where to save the plots.
.saving_image_dir_String defining the sub-folder in the path where to save the plots.
.saving_image_scale_Positive integer dictating the scale at which the image is saved. Default value is 5 for images with width of 1/3 an A4 paper.
.saving_image_width_Positive integer setting the width of the saved version of the plot. The default (1,000 px) is appropriate for an image 1/3 of the width of A4 sheet.
.saving_image_height_Positive integer setting the height of the saved version of the plot. The default (600 px) is appropriate for an image 1/3 of the width of A4 sheet.
.saving_image_units_A string (default "px") defining the units in which the width and height are provided.
Examples
\dontrun{
}
Method draw_PSA_summary_tables()
Draw PSA summary tables:—-
Usage
calibR_R6$draw_PSA_summary_tables( .save_ = FALSE, .saving_path_ = here::here(), .saving_data_dir_ = "/data/PSA tables/" )
Arguments
.save_Logical for whether to save tables data.
.saving_path_String defining the path for where to save the tables data.
.saving_data_dir_String defining the sub-folder in the path where to save the tables data.
Examples
\dontrun{
}
Method clone()
The objects of this class are cloneable with this method.
Usage
calibR_R6$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
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## Method `calibR_R6$new`
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## Method `calibR_R6$sampleR`
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## Method `calibR_R6$calibrateR_random`
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## Method `calibR_R6$calibrateR_directed`
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## Method `calibR_R6$calibrateR_bayesian`
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## Method `calibR_R6$sample_PSA_values`
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## Method `calibR_R6$run_PSA`
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## Method `calibR_R6$summarise_PSA`
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## Method `calibR_R6$draw_plots`
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## Method `calibR_R6$draw_GOF_measure`
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## Method `calibR_R6$draw_targets_plots`
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## Method `calibR_R6$draw_distributions_plots`
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## Method `calibR_R6$draw_PSA_summary_tables`
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