crcmodel: R6 Class Representing a 'crcmodel'
In c-rutter/crcrdm: Robust Decision Making tools for cancer screening models
crcmodel R Documentation
R6 Class Representing a 'crcmodel'
Description
This class implements a 'crcmodel'.
Public fields
nameis a character string representing the model name (e.g. CRC-SPIN v 2.1)
inputsis a list of model inputs. One can add inputs to the object with the set_input function.
inputs_tableis a data.frame listing all inputs added to the model.
posterior_paramsis a data.frame containing parameters from the posterior distribution of the model. This table is set with the set_posterior function.
natural_history_resultsis the object that will receive results from the natural history function.
screening_resultsis the object that will receive results from the natural history function.
simulate_natural_history_fnis a function that will take as parameters self and ..., simulate the natural history of crc and returns invisible(self).
simulate_screening_fnis a function that will take as parameters self and ..., simulate the natural history of crc and returns invisible(self).
Methods
Public methods
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Method new()
Create a new 'crcmodel' object.
Usage
crcmodel$new(name)
Arguments
namename of the model to be created.
Returns
s new 'crcmodel' object.
Method set_input()
Set Input
Usage
crcmodel$set_input(name, value, type)
Arguments
namecharacter string defining the input name
valueinput value. Can be a single value, a list or a vector.
typeFunctionality TBD, but inputs can be filtered by type.
Details
Use this function to add a new input to a crcmodel object.
All model inputs shoulhd be added or modified through this function. Inputs can be vectors or lists of strings, numeric or integers, to guarantee that they can be translated to and from JSON without any issues.
Method to_json()
Converts a 'crcmodel' to a JSON string
Usage
crcmodel$to_json(input_types)
Arguments
input_typesvector of input types to include in the json object.
Returns
a JSON string containing the crcmodel objects that should be exported
Method set_inputs_from_json()
Set model Inputs from JSON string
Usage
crcmodel$set_inputs_from_json(json)
Arguments
jsona JSON string generated by the model_to_json function
Details
Use this function to set model inputs from a JSON string. Note that the posterior distribution is not included in the json strong of model inputs.
Method set_posterior()
Set Posterior distribution of model parameters
Usage
crcmodel$set_posterior(
posteriors_list,
posterior_weights,
cols_to_ignore = NULL,
n_posterior = 1000,
use_average = F,
seed = 12345678,
resample = T
)
Arguments
posteriors_listnamed list of one more more data.frames containing the posterior of model parameters.
posterior_weightscharacter indicating the name of the column that contain weights to be used when sampling from the posterior
cols_to_ignorecharacter vector of columns name to ignore. This is useful when the posterior files contain columns that are not parameters and are not inputs to the model.
n_posteriorthe size of the sample to take from each posterior file.
use_averageT if one wants to use the average value of all parameters rather than the mean
seedrandom seed to use when sampling from the posterior
resampleif T, samples from the posterior using the posterior weights. Otherwise, returns the full posterior, and preservers the weights.
Details
Use this function to add a new input to a crcmodel object.
All model inputs shoulhd be added or modified through this function. Inputs can be vectors or lists of strings, numeric or integers, to guarantee that they can be translated to and from JSON without any issues.
Method set_natural_history_fn()
Set Natural History Function
Usage
crcmodel$set_natural_history_fn(
natural_history_fn,
returns_standardized_df = T
)
Arguments
natural_history_fnan R function that is capable of simulating the model's natural history.
returns_standardized_dfif TRUE, your natural history function must return a standardized dataframe.
Details
This function allows the user to have a custom natural history function. Having this flexibility effectively decouples the model itself from the package that is used to simulate the model. As in a regression, the functional form of the model is not baked into 'lm'. Instead, 'lm' allows you to create *any* linear model.
Use this function to define a natural history function. You are advised to create a simple natural history function and put a 'browser()' inside it. Then, after creating your model with 'my_model <- crcmodel$new()' and adding inputs with 'my_model$set_input()', assign your custom natural history function with 'my_model$set_natural_history(my_function_name)'. 'my_function_name' should the name of a function that you already sourced from a script or from a package. Finally, run 'my_model$simulate_natural_history()' to run your natural history model. You will then see all your inputs inside the 'self' object, which is an environment that contains all the inputs for your model.
Method set_screening_fn()
Set Screening Function
Usage
crcmodel$set_screening_fn(screening_fn, returns_standardized_df = T)
Arguments
screening_fnan R function that is capable of simulating the model's screening.
returns_standardized_dfif TRUE, your natural history function must return a standardized dataframe.
Details
This function allows the user to have a custom screening function.
Method simulate_natural_history()
Simulate Natural History Wrapper Function
Usage
crcmodel$simulate_natural_history(...)
Arguments
...any set of parameters passed to this function will be passed along to the user natural history function.
Details
This function is a wrapper around the user natural history function. It passes the 'self' object and any other parameters provided to the function.
Method simulate_screening()
Simulate Screening Wrapper Function
Usage
crcmodel$simulate_screening(...)
Arguments
...any set of parameters passed to this function will be passed along to the user screening function.
Details
This function is a wrapper around the user screening. It passes the 'self' object and any other parameters provided to the function.
Method clone()
The objects of this class are cloneable with this method.
Usage
crcmodel$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
set_natural_history_fn
set_natural_history_fn
set_screening_fn
c-rutter/crcrdm documentation built on July 1, 2023, 7:35 a.m.
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| crcmodel | R Documentation |
R6 Class Representing a 'crcmodel'
Description
This class implements a 'crcmodel'.
Public fields
nameis a character string representing the model name (e.g. CRC-SPIN v 2.1)
inputsis a list of model inputs. One can add inputs to the object with the set_input function.
inputs_tableis a data.frame listing all inputs added to the model.
posterior_paramsis a data.frame containing parameters from the posterior distribution of the model. This table is set with the set_posterior function.
natural_history_resultsis the object that will receive results from the natural history function.
screening_resultsis the object that will receive results from the natural history function.
simulate_natural_history_fnis a function that will take as parameters self and ..., simulate the natural history of crc and returns invisible(self).
simulate_screening_fnis a function that will take as parameters self and ..., simulate the natural history of crc and returns invisible(self).
Methods
Public methods
Method new()
Create a new 'crcmodel' object.
Usage
crcmodel$new(name)
Arguments
namename of the model to be created.
Returns
s new 'crcmodel' object.
Method set_input()
Set Input
Usage
crcmodel$set_input(name, value, type)
Arguments
namecharacter string defining the input name
valueinput value. Can be a single value, a list or a vector.
typeFunctionality TBD, but inputs can be filtered by type.
Details
Use this function to add a new input to a crcmodel object. All model inputs shoulhd be added or modified through this function. Inputs can be vectors or lists of strings, numeric or integers, to guarantee that they can be translated to and from JSON without any issues.
Method to_json()
Converts a 'crcmodel' to a JSON string
Usage
crcmodel$to_json(input_types)
Arguments
input_typesvector of input types to include in the json object.
Returns
a JSON string containing the crcmodel objects that should be exported
Method set_inputs_from_json()
Set model Inputs from JSON string
Usage
crcmodel$set_inputs_from_json(json)
Arguments
jsona JSON string generated by the model_to_json function
Details
Use this function to set model inputs from a JSON string. Note that the posterior distribution is not included in the json strong of model inputs.
Method set_posterior()
Set Posterior distribution of model parameters
Usage
crcmodel$set_posterior( posteriors_list, posterior_weights, cols_to_ignore = NULL, n_posterior = 1000, use_average = F, seed = 12345678, resample = T )
Arguments
posteriors_listnamed list of one more more data.frames containing the posterior of model parameters.
posterior_weightscharacter indicating the name of the column that contain weights to be used when sampling from the posterior
cols_to_ignorecharacter vector of columns name to ignore. This is useful when the posterior files contain columns that are not parameters and are not inputs to the model.
n_posteriorthe size of the sample to take from each posterior file.
use_averageT if one wants to use the average value of all parameters rather than the mean
seedrandom seed to use when sampling from the posterior
resampleif T, samples from the posterior using the posterior weights. Otherwise, returns the full posterior, and preservers the weights.
Details
Use this function to add a new input to a crcmodel object. All model inputs shoulhd be added or modified through this function. Inputs can be vectors or lists of strings, numeric or integers, to guarantee that they can be translated to and from JSON without any issues.
Method set_natural_history_fn()
Set Natural History Function
Usage
crcmodel$set_natural_history_fn( natural_history_fn, returns_standardized_df = T )
Arguments
natural_history_fnan R function that is capable of simulating the model's natural history.
returns_standardized_dfif TRUE, your natural history function must return a standardized dataframe.
Details
This function allows the user to have a custom natural history function. Having this flexibility effectively decouples the model itself from the package that is used to simulate the model. As in a regression, the functional form of the model is not baked into 'lm'. Instead, 'lm' allows you to create *any* linear model. Use this function to define a natural history function. You are advised to create a simple natural history function and put a 'browser()' inside it. Then, after creating your model with 'my_model <- crcmodel$new()' and adding inputs with 'my_model$set_input()', assign your custom natural history function with 'my_model$set_natural_history(my_function_name)'. 'my_function_name' should the name of a function that you already sourced from a script or from a package. Finally, run 'my_model$simulate_natural_history()' to run your natural history model. You will then see all your inputs inside the 'self' object, which is an environment that contains all the inputs for your model.
Method set_screening_fn()
Set Screening Function
Usage
crcmodel$set_screening_fn(screening_fn, returns_standardized_df = T)
Arguments
screening_fnan R function that is capable of simulating the model's screening.
returns_standardized_dfif TRUE, your natural history function must return a standardized dataframe.
Details
This function allows the user to have a custom screening function.
Method simulate_natural_history()
Simulate Natural History Wrapper Function
Usage
crcmodel$simulate_natural_history(...)
Arguments
...any set of parameters passed to this function will be passed along to the user natural history function.
Details
This function is a wrapper around the user natural history function. It passes the 'self' object and any other parameters provided to the function.
Method simulate_screening()
Simulate Screening Wrapper Function
Usage
crcmodel$simulate_screening(...)
Arguments
...any set of parameters passed to this function will be passed along to the user screening function.
Details
This function is a wrapper around the user screening. It passes the 'self' object and any other parameters provided to the function.
Method clone()
The objects of this class are cloneable with this method.
Usage
crcmodel$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
set_natural_history_fn
set_natural_history_fn
set_screening_fn
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