Package 'embr' reference manual

Title:	Model Builder Utility Functions and Virtual Classes
Description:	Utility functions and virtual classes shared by model builder packages such as tmbr, jmbr and smbr.
Authors:	Joe Thorley [aut, cre] , Kirill Müller [aut] , Ayla Pearson [ctb] , Nadine Hussein [ctb] , Poisson Consulting [cph, fnd]
Maintainer:	Joe Thorley <[email protected]>
License:	MIT + file LICENSE
Version:	0.0.1.9037
Built:	2024-11-01 16:19:48 UTC
Source:	https://github.com/poissonconsulting/embr

Analyse

Description

Analyse

Usage

analyse(x, ...)
analyse(x, ...)

Arguments

`x`	The object to analyse.
`...`	Additional arguments.

Analyse Residuals

Description

Analyse Residuals

Usage

analyse_residuals(x)
analyse_residuals(x)

Arguments

`x`	The mb_analysis object to analyse the residuals for.

Analyse

Description

Analyse

Usage

## S3 method for class 'character'
analyse(
  x,
  data,
  select_data = list(),
  nchains = getOption("mb.nchains", 3L),
  niters = getOption("mb.niters", 1000L),
  nthin = getOption("mb.nthin", 1L),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)
## S3 method for class 'character'
analyse(
  x,
  data,
  select_data = list(),
  nchains = getOption("mb.nchains", 3L),
  niters = getOption("mb.niters", 1000L),
  nthin = getOption("mb.nthin", 1L),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)

Arguments

`x`	An object inheriting from class mb_model or a list of such objects.
`data`	The data frame to analyse.
`select_data`	A named list specifying the columns to select and their associated classes and values as well as transformations and scaling options.
`nchains`	A count of the number of chains.
`niters`	A count of the number of simulations to save per chain.
`nthin`	A count of the thining interval or NULL (in which case taken from model).
`parallel`	A flag indicating whether to perform the analysis in parallel if possible.
`quiet`	A flag indicating whether to disable tracing information.
`glance`	A flag indicating whether to print a model summary.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Additional arguments.

Analyse

Description

Analyse

Usage

## S3 method for class 'mb_model'
analyse(
  x,
  data,
  nchains = getOption("mb.nchains", 3L),
  niters = getOption("mb.niters", 1000L),
  nthin = getOption("mb.thin", NULL),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)
## S3 method for class 'mb_model'
analyse(
  x,
  data,
  nchains = getOption("mb.nchains", 3L),
  niters = getOption("mb.niters", 1000L),
  nthin = getOption("mb.thin", NULL),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)

Arguments

`x`	An object inheriting from class mb_model or a list of such objects.
`data`	The data frame to analyse.
`nchains`	A count of the number of chains.
`niters`	A count of the number of simulations to save per chain.
`nthin`	A count of the thining interval or NULL (in which case taken from model).
`parallel`	A flag indicating whether to perform the analysis in parallel if possible.
`quiet`	A flag indicating whether to disable tracing information.
`glance`	A flag indicating whether to print a model summary.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Additional arguments.

Analyse

Description

Analyse

Usage

## S3 method for class 'mb_models'
analyse(
  x,
  data,
  nchains = getOption("mb.nchains", 3L),
  niters = getOption("mb.niters", 1000L),
  nthin = getOption("mb.thin", NULL),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)
## S3 method for class 'mb_models'
analyse(
  x,
  data,
  nchains = getOption("mb.nchains", 3L),
  niters = getOption("mb.niters", 1000L),
  nthin = getOption("mb.thin", NULL),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)

Arguments

`x`	An object inheriting from class mb_model or a list of such objects.
`data`	The data frame to analyse.
`nchains`	A count of the number of chains.
`niters`	A count of the number of simulations to save per chain.
`nthin`	A count of the thining interval or NULL (in which case taken from model).
`parallel`	A flag indicating whether to perform the analysis in parallel if possible.
`quiet`	A flag indicating whether to disable tracing information.
`glance`	A flag indicating whether to print a model summary.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Additional arguments.

Analyse

Description

Analyse

Usage

analyse1(
  model,
  data,
  loaded,
  nchains,
  niters,
  nthin,
  quiet,
  glance,
  parallel,
  ...
)
analyse1(
  model,
  data,
  loaded,
  nchains,
  niters,
  nthin,
  quiet,
  glance,
  parallel,
  ...
)

Arguments

`model`	The mb_model to analyse.
`data`	The data.
`loaded`	The loaded model.
`nchains`	chains.
`niters`	iters
`nthin`	thin
`quiet`	quiet
`glance`	glance
`parallel`	parallel
`...`	Additional arguments.

MB Analyses

Description

Creates an object inherting from class mb_analyses.

Usage

analyses(...)
analyses(...)

Arguments

...

Named objects.

Value

An object inheriting from class mb_analyses.

Coerce to an mb_analyses object

Description

Coerce to an mb_analyses object

Usage

as.analyses(x, ...)
as.analyses(x, ...)

Arguments

`x`	object to coerce.
`...`	Unused.

Coerce to an mb_model object

Description

Coerce to an mb_model object

Usage

as.model(x, ...)
as.model(x, ...)

Arguments

`x`	object to coerce.
`...`	Unused.

Coerce to an mb_models object

Description

Coerce to an mb_models object

Usage

as.models(x, ...)
as.models(x, ...)

Arguments

`x`	object to coerce.
`...`	Unused.

Backwards

Description

Perform backwards step-wise regression on a model. Returns a list of the analysis at each step starting with the full model.

Usage

backwards(
  model,
  data,
  drops = list(),
  conf_level = getOption("mb.conf_level", 0.95),
  beep = getOption("mb.beep", TRUE),
  ...
)
backwards(
  model,
  data,
  drops = list(),
  conf_level = getOption("mb.conf_level", 0.95),
  beep = getOption("mb.beep", TRUE),
  ...
)

Arguments

`model`	An object.
`data`	Data.
`drops`	A list of character vectors specifying the scalar parameters to consider.
`conf_level`	A number specifying the confidence level. By default 0.95.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Unused.

Details

drop is a list of character vectors specifying the scalar parameters to possibly drop. If a list element consists of two or more strings then the earlier strings are only available to drop after the later strings have been eliminated. This allows polynomial dependencies to be respected.

Value

A list of the analyses.

Base Model

Description

Base Model

Usage

base_model(model, drops = list())
base_model(model, drops = list())

Arguments

`model`	The full model.
`drops`	A list of character vectors specifying possible drops.

Value

The base model (with all drops).

Check MB Analysis

Description

Check MB Analysis

Usage

check_mb_analysis(object, object_name = substitute(object))
check_mb_analysis(object, object_name = substitute(object))

Arguments

`object`	The object to check.
`object_name`	A string of the object name.

Value

The object or throws an informative error.

Check MB Code

Description

Check MB Code

Usage

check_mb_code(object, object_name = substitute(object))
check_mb_code(object, object_name = substitute(object))

Arguments

`object`	The object to check.
`object_name`	A string of the object name.

Value

The object or throws an informative error.

Check MB Model

Description

Check MB Model

Usage

check_mb_model(object, object_name = substitute(object))
check_mb_model(object, object_name = substitute(object))

Arguments

`object`	The object to check.
`object_name`	A string of the object name.

Value

The object or throws an informative error.

Check Model Parameters

Description

Check Model Parameters

Usage

check_model_pars(x, fixed, random, derived, drops)
check_model_pars(x, fixed, random, derived, drops)

Arguments

`x`	The model code to check.
`fixed`	A string of a regular expression specifying the fixed parameters to monitor.
`random`	NULL or a character vector of the random effects.
`derived`	NULL or a character vector of the derived parameters.
`drops`	NULL or a character vector of the parameters to drop.

Value

The possibly updated derived parameters.

Check Uniquely Named List

Description

Check Uniquely Named List

Usage

check_uniquely_named_list(x, x_name = substitute(x))
check_uniquely_named_list(x, x_name = substitute(x))

Arguments

`x`	The object to check.
`x_name`	A string of the objects name.

Value

The original object or throws an informative error.

Code

Description

Gets the MB code for an object.

Usage

code(object, ...)
code(object, ...)

Arguments

`object`	The object.
`...`	Additional arguments.

Value

An object inheriting from class mb_code.

Coef Profile

Description

Gets coef confidence limits by likelihood profiling.

Usage

coef_profile(object, ...)
coef_profile(object, ...)

Arguments

`object`	The object.
`...`	Unused.

Coef TMB Analyses

Description

Coefficients for fixed parameters from an ML based MB analyses averaged by IC weights.

Usage

## S3 method for class 'mb_analyses'
coef_profile(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  parallel = getOption("mb.parallel", FALSE),
  beep = getOption("mb.beep", TRUE),
  ...
)
## S3 method for class 'mb_analyses'
coef_profile(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  parallel = getOption("mb.parallel", FALSE),
  beep = getOption("mb.beep", TRUE),
  ...
)

Arguments

`object`	The mb_analyses object.
`param_type`	A flag specifying whether 'fixed', 'random' or 'derived' terms.
`include_constant`	A flag specifying whether to include constant terms.
`conf_level`	A number specifying the confidence level. By default 0.95.
`estimate`	The function to use to calculate the estimate for Bayesian models.
`parallel`	A flag indicating whether to using parallel backend provided by foreach.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Not used.

Value

A tidy tibble of the coeffcient terms with the model averaged estimate, the Akaike's weight and the proportion of models including the term.

Coef Profile Analysis

Description

Coefficients for a analysis.

Usage

## S3 method for class 'mb_analysis'
coef_profile(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  parallel = getOption("mb.parallel", FALSE),
  beep = getOption("mb.profile", TRUE),
  simplify = TRUE,
  ...
)
## S3 method for class 'mb_analysis'
coef_profile(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  parallel = getOption("mb.parallel", FALSE),
  beep = getOption("mb.profile", TRUE),
  simplify = TRUE,
  ...
)

Arguments

`object`	The mb_analysis object.
`param_type`	A flag specifying whether 'fixed', 'random' or 'derived' terms.
`include_constant`	A flag specifying whether to include constant terms.
`conf_level`	A number specifying the confidence level. By default 0.95.
`estimate`	The function to use to calculating the estimate for Bayesian models.
`parallel`	A flag indicating whether to using parallel backend provided by foreach.
`beep`	A flag indicating whether to beep on completion of the analysis.
`simplify`	A flag specifying whether to simplify with svalue.
`...`	Not used.

Details

The zscore is mean / sd.

Value

A tidy tibble of the coefficient terms.

Coef TMB Meta Analyses

Description

Coef TMB Meta Analyses

Usage

## S3 method for class 'mb_meta_analyses'
coef_profile(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  parallel = getOption("mb.parallel", FALSE),
  beep = getOption("mb.parallel", TRUE),
  ...
)
## S3 method for class 'mb_meta_analyses'
coef_profile(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  parallel = getOption("mb.parallel", FALSE),
  beep = getOption("mb.parallel", TRUE),
  ...
)

Arguments

`object`	The mb_meta_analyses object.
`param_type`	A flag specifying whether 'fixed', 'random' or 'derived' terms.
`include_constant`	A flag specifying whether to include constant terms.
`conf_level`	A number specifying the confidence level. By default 0.95.
`estimate`	The function to use to calculate the estimate.
`parallel`	A flag indicating whether to using parallel backend provided by foreach.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Not used.

Value

A tidy tibble.

Coef TMB Meta Analysis

Description

Coef TMB Meta Analysis

Usage

## S3 method for class 'mb_meta_analysis'
coef_profile(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  parallel = getOption("mb.parallel", FALSE),
  beep = getOption("mb.beep", TRUE),
  ...
)
## S3 method for class 'mb_meta_analysis'
coef_profile(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  parallel = getOption("mb.parallel", FALSE),
  beep = getOption("mb.beep", TRUE),
  ...
)

Arguments

`object`	The mb_meta_analysis object.
`param_type`	A flag specifying whether 'fixed', 'random' or 'derived' terms.
`include_constant`	A flag specifying whether to include constant terms.
`conf_level`	A number specifying the confidence level. By default 0.95.
`estimate`	The function to use to calculate the estimate.
`parallel`	A flag indicating whether to using parallel backend provided by foreach.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Not used.

Value

A tidy tibble.

Coef TMB Analyses

Description

Coefficients for fixed parameters from an ML based MB analyses averaged by IC weights.

Usage

## S3 method for class 'mb_analyses'
coef(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  ...
)
## S3 method for class 'mb_analyses'
coef(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  ...
)

Arguments

`object`	The mb_analyses object.
`param_type`	A flag specifying whether 'fixed', 'random' or 'derived' terms.
`include_constant`	A flag specifying whether to include constant terms.
`conf_level`	A number specifying the confidence level. By default 0.95.
`estimate`	The function to use to calculate the estimate for Bayesian models.
`...`	Not used.

Value

A tidy tibble of the coeffcient terms with the model averaged estimate, the Akaike's weight and the proportion of models including the term.

Coef JAGS Analysis

Description

Coefficients for a JAGS analysis.

Usage

## S3 method for class 'mb_analysis'
coef(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  simplify = FALSE,
  ...
)
## S3 method for class 'mb_analysis'
coef(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  simplify = FALSE,
  ...
)

Arguments

`object`	The mb_analysis object.
`param_type`	A flag specifying whether 'fixed', 'random' or 'derived' terms.
`include_constant`	A flag specifying whether to include constant terms.
`conf_level`	A number specifying the confidence level. By default 0.95.
`estimate`	The function to use to calculating the estimate for Bayesian models.
`simplify`	A flag specifying whether to drop sd and zscore columns and return svalue instead of pvalue.
`...`	Not used.

Details

The zscore is mean / sd.

Value

A tidy tibble of the coefficient terms.

Coef TMB Meta Analyses

Description

Coef TMB Meta Analyses

Usage

## S3 method for class 'mb_meta_analyses'
coef(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  ...
)
## S3 method for class 'mb_meta_analyses'
coef(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  ...
)

Arguments

`object`	The mb_meta_analyses object.
`param_type`	A flag specifying whether 'fixed', 'random' or 'derived' terms.
`include_constant`	A flag specifying whether to include constant terms.
`conf_level`	A number specifying the confidence level. By default 0.95.
`estimate`	The function to use to calculate the estimate.
`...`	Not used.

Value

A tidy tibble.

Coef TMB Meta Analysis

Description

Coef TMB Meta Analysis

Usage

## S3 method for class 'mb_meta_analysis'
coef(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  ...
)
## S3 method for class 'mb_meta_analysis'
coef(
  object,
  param_type = "fixed",
  include_constant = TRUE,
  conf_level = getOption("mb.conf_level", 0.95),
  estimate = getOption("mb.estimate", median),
  ...
)

Arguments

`object`	The mb_meta_analysis object.
`param_type`	A flag specifying whether 'fixed', 'random' or 'derived' terms.
`include_constant`	A flag specifying whether to include constant terms.
`conf_level`	A number specifying the confidence level. By default 0.95.
`estimate`	The function to use to calculate the estimate.
`...`	Not used.

Value

A tidy tibble.

Comment String

Description

Returns the regular expression used to identify the start of a comment in an mb_code object.

Usage

comment_string(object, ...)
comment_string(object, ...)

Arguments

`object`	The mb code object.
`...`	Unused.

Value

A string of the regular expression.

Data Set

Description

Gets the data set for an object inheriting from class mb_analysis.

Usage

data_set(
  x,
  modify = FALSE,
  numericize_factors = FALSE,
  marginalize_random_effects = FALSE,
  ...
)
data_set(
  x,
  modify = FALSE,
  numericize_factors = FALSE,
  marginalize_random_effects = FALSE,
  ...
)

Arguments

`x`	The object.
`modify`	A flag indicating whether to modify the data.
`numericize_factors`	A flag indicating whether to convert factors to integers if modifying the data.
`marginalize_random_effects`	A flag indicating whether to set each factor in one or more random effects at its first level.
`...`	Unused.

Value

The data set as a tibble.

Density99

Description

dummy data

Usage

density99
density99

Format

An object of class data.frame with 300 rows and 5 columns.

Drop pars

Description

Drops named scalar fixed pars from an object by fixing them at 0.

Usage

drop_pars(x, pars = character(0), ...)
drop_pars(x, pars = character(0), ...)

Arguments

`x`	The object.
`pars`	A character vector of the pars to drop.
`...`	Not used.

Value

The updated object.

Elapsed

Description

Get elapsed duration.

Usage

elapsed(x, ...)
elapsed(x, ...)

Arguments

`x`	The object to calculate it for.
`...`	Not used.

Estimates

Description

Calculates the estimates for an MCMC object.

Usage

## S3 method for class 'mb_analysis'
estimates(x, param_type = "fixed", ...)
## S3 method for class 'mb_analysis'
estimates(x, param_type = "fixed", ...)

Arguments

`x`	An object.
`param_type`	A string indicating the type of terms to get the names for.
`...`	Other arguments passed to methods.

Value

A list of uniquely named numeric objects.

Fitted Values

Description

Extract fitted values for a MB analysis.

Usage

## S3 method for class 'mb_analysis'
fitted(object, ...)
## S3 method for class 'mb_analysis'
fitted(object, ...)

Arguments

`object`	The MB analysis object.
`...`	Unused.

Details

The new_expr in the model must include the term 'fit'.

Value

The analysis data set with the fitted values.

Get Analysis Mode

Description

Gets analysis mode.

Usage

get_analysis_mode()
get_analysis_mode()

Details

Retrieves what is set for each of the following package options.

mb.nchains: A count of the number of chains.
mb.niters: A count of the number of simulations to save per chain.
mb.nthin: A count of the thining interval.
mb.parallel: A flag indicating whether to perform the analysis in parallel.
mb.quiet: A flag indicating whether to disable tracing information.
mb.beep: A flag indicating whether to beep on completion of the analysis.
mb.glance: A flag indicating whether to print a model summary.
mb.nreanalyses: A count specifying the maximum number of reanalyses.
mb.rhat: A number specifying the rhat threshold.
mb.esr: A number specifying the minimum effective sampling rate.
mb.duration: The maximum total time to spend on analysis and reanalysis.
mb.conf_level: A number specifying the confidence level.

Value

A named list of the current package options.

Examples

## Not run: 
get_analysis_mode()

## End(Not run)
## Not run: 
get_analysis_mode()

## End(Not run)

Retrieve model

Description

Constucts a new analysis object

Usage

get_model(analysis)
get_model(analysis)

Arguments

analysis

An object of class '"mb_analysis"'.

Information Criterion

Description

Calculates Information Criterion for an analysis.

Usage

IC(object, ...)
IC(object, ...)

Arguments

`object`	The object to calculate the IC for.
`...`	Not used.

Value

The Information Criteron as a number.

Test if is bayesian

Description

Test if is bayesian

Usage

is_bayesian(x, ...)
is_bayesian(x, ...)

Arguments

`x`	the object.
`...`	Unused

Value

A flag indicating wether bayesian.

Test if is frequentist

Description

Test if is frequentist

Usage

is_frequentist(x)
is_frequentist(x)

Arguments

`x`	the object.

Value

A flag indicating whether frequentist

Is Named List

Description

Is Named List

Usage

is_namedlist(x)
is_namedlist(x)

Arguments

`x`	The object to test.

Value

A flag.

Examples

is_namedlist(1)
is_namedlist(list())
is_namedlist(list(1))
is_namedlist(list(x = 1))
is_namedlist(list(x = list(y = 2)))
is_namedlist(1)
is_namedlist(list())
is_namedlist(list(1))
is_namedlist(list(x = 1))
is_namedlist(list(x = list(y = 2)))

Is a LMB Analysis

Description

Tests whether x is an object of class 'lmb_analysis'

Usage

is.lmb_analysis(x)
is.lmb_analysis(x)

Arguments

`x`	The object to test.

Value

A flag indicating whether the test was positive.

Is a LMB Code

Description

Tests whether x is an object of class 'lmb_code'

Usage

is.lmb_code(x)
is.lmb_code(x)

Arguments

`x`	The object to test.

Value

A flag indicating whether the test was positive.

Is a LMB Model

Description

Tests whether x is an object of class 'lmb_model'

Usage

is.lmb_model(x)
is.lmb_model(x)

Arguments

`x`	The object to test.

Value

A flag indicating whether the test was positive.

Is MB Analyses?

Description

Tests whether x is an object of class 'mb_analyses'

Usage

is.mb_analyses(x)
is.mb_analyses(x)

Arguments

`x`	The object to test.

Value

A flag indicating whether the test was positive.

Is MB Analysis?

Description

Tests whether x is an object of class 'mb_analysis'

Usage

is.mb_analysis(x)
is.mb_analysis(x)

Arguments

`x`	The object to test.

Value

A flag indicating whether the test was positive.

Is MB Code?

Description

Tests whether x is an object of class 'mb_model'

Usage

is.mb_code(x)
is.mb_code(x)

Arguments

`x`	The object to test.

Value

A flag indicating whether the test was positive.

Is MB Model?

Description

Tests whether x is an object of class 'mb_model'

Usage

is.mb_model(x)
is.mb_model(x)

Arguments

`x`	The object to test.

Value

A flag indicating whether the test was positive.

Is MB Models?

Description

Tests whether x is an object of class 'mb_models'

Usage

is.mb_models(x)
is.mb_models(x)

Arguments

`x`	The object to test.

Value

A flag indicating whether the test was positive.

Is MB Null Analysis?

Description

Tests whether x is an object of class 'mb_null_analysis'

Usage

is.mb_null_analysis(x)
is.mb_null_analysis(x)

Arguments

`x`	The object to test.

Value

A flag indicating whether the test was positive.

Is syntactic

Description

Is syntactic

Usage

is.syntactic(x)
is.syntactic(x)

Arguments

`x`	A character of possible variable names.

Value

A logical vector indicating whether a syntactically correct variable name.

Examples

is.syntactic(c("0", "x", "1x", "x y", "x1"))
is.syntactic(c("0", "x", "1x", "x y", "x1"))

Load Model

Description

Load Model

Usage

load_model(x, quiet, ...)
load_model(x, quiet, ...)

Arguments

`x`	The model to load.
`quiet`	A flag indicating whether to suppress warnings and output.
`...`	Additional arguments.

Log-Likelihood

Description

Log-Likelihood for a MB analysis.

Usage

## S3 method for class 'mb_analysis'
logLik(object, ...)
## S3 method for class 'mb_analysis'
logLik(object, ...)

Arguments

`object`	The mb_analysis object.
`...`	unused.

Log-Likelihood

Description

Log-Likelihood for a MB NULL analysis.

Usage

## S3 method for class 'mb_null_analysis'
logLik(object, ...)
## S3 method for class 'mb_null_analysis'
logLik(object, ...)

Arguments

`object`	The mb_analysis object.
`...`	unused.

Make All Models

Description

Make All Models

Usage

make_all_models(model, drops = list())
make_all_models(model, drops = list())

Arguments

`model`	The full model.
`drops`	A list of character vectors specifying possible drops.

Value

A list of objects inheriting from class mb_model.

MB Code

Description

Identifies the type of the code and creates an object of the appropriate class.

Usage

mb_code(template)

new_mb_code(x, class)
mb_code(template)

new_mb_code(x, class)

Arguments

`template`	A string, a braced '' expression (unquoted or quoted), or an object of class '"mb_code"'.
`x`	A string or a braced '' expression.
`class`	The class of the new object.

Value

An object inheriting from class mb_code.

Examples

x <- mb_code(
  "#include <TMB.hpp>

template<class Type>

Type objective_function<Type>::operator() () {
DATA_VECTOR(Count);
PARAMETER(bIntercept);

int n = Count.size();

Type nll = 0.0;
for(int i = 0; i < n; i++){
  nll -= dpois(Count(i), bIntercept, true);
}
return nll;
}
"
)
class(x)
x <- mb_code(
  "#include <TMB.hpp>

template<class Type>

Type objective_function<Type>::operator() () {
DATA_VECTOR(Count);
PARAMETER(bIntercept);

int n = Count.size();

Type nll = 0.0;
for(int i = 0; i < n; i++){
  nll -= dpois(Count(i), bIntercept, true);
}
return nll;
}
"
)
class(x)

Derive Data

Description

Calculate derived parameters.

Usage

## S3 method for class 'mb_analyses'
mcmc_derive_data(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  modify_new_data = NULL,
  ref_data = FALSE,
  ref_fun2 = proportional_change2,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)
## S3 method for class 'mb_analyses'
mcmc_derive_data(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  modify_new_data = NULL,
  ref_data = FALSE,
  ref_fun2 = proportional_change2,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)

Arguments

`object`	An object inheriting from class mb_analysis.
`new_data`	The data frame to calculate the predictions for.
`new_expr`	A string of R code specifying the predictive relationship.
`new_values`	A named list of new or replacement values to pass to new_expr.
`term`	A string of the term in new_expr.
`modify_new_data`	A single argument function to modify new data (in list form) immediately prior to calculating new_expr.
`ref_data`	A flag or a data frame with 1 row indicating the reference values for calculating the effects size.
`ref_fun2`	A function whose first argument takes a vector of two numbers and returns a scalar of a metric of the difference between them.
`new_expr_vec`	A flag specifying whether to vectorize the new_expr code.
`parallel`	A flag indicating whether to do predictions using parallel backend provided by foreach.
`quiet`	A flag indicating whether to disable tracing information.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Additional arguments.

Value

A object of class mcmc_data.

Derive Data

Description

Calculate derived parameters.

Usage

## S3 method for class 'mb_analysis'
mcmc_derive_data(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  modify_new_data = NULL,
  ref_data = FALSE,
  ref_fun2 = proportional_change2,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  random_effects = NULL,
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)
## S3 method for class 'mb_analysis'
mcmc_derive_data(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  modify_new_data = NULL,
  ref_data = FALSE,
  ref_fun2 = proportional_change2,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  random_effects = NULL,
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)

Arguments

`object`	An object inheriting from class mb_analysis.
`new_data`	The data frame to calculate the predictions for.
`new_expr`	A string of R code specifying the predictive relationship.
`new_values`	A named list of new or replacement values to pass to new_expr.
`term`	A string of the term in new_expr.
`modify_new_data`	A single argument function to modify new data (in list form) immediately prior to calculating new_expr.
`ref_data`	A flag or a data frame with 1 row indicating the reference values for calculating the effects size.
`ref_fun2`	A function whose first argument takes a vector of two numbers and returns a scalar of a metric of the difference between them.
`new_expr_vec`	A flag specifying whether to vectorize the new_expr code.
`random_effects`	A named list specifying the random effects and the associated factors.
`parallel`	A flag indicating whether to do predictions using parallel backend provided by foreach.
`quiet`	A flag indicating whether to disable tracing information.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Additional arguments.

Value

A object of class mcmc_data.

Derive

Description

Calculate derived parameters.

Usage

## S3 method for class 'mb_analyses'
mcmc_derive(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  modify_new_data = NULL,
  ref_data = FALSE,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)
## S3 method for class 'mb_analyses'
mcmc_derive(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  modify_new_data = NULL,
  ref_data = FALSE,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)

Arguments

`object`	An object inheriting from class mb_analysis.
`new_data`	The data frame to calculate the predictions for.
`new_expr`	A string of R code specifying the predictive relationship.
`new_values`	A named list of new or replacement values to pass to new_expr.
`term`	A string of the term in new_expr.
`modify_new_data`	A single argument function to modify new data (in list form) immediately prior to calculating new_expr.
`ref_data`	A flag or a data frame with 1 row indicating the reference values for calculating the effects size.
`new_expr_vec`	A flag specifying whether to vectorize the new_expr code.
`parallel`	A flag indicating whether to do predictions using parallel backend provided by foreach.
`quiet`	A flag indicating whether to disable tracing information.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Additional arguments.

Value

A object of class mcmcr.

Derive

Description

Calculate derived parameters.

Usage

## S3 method for class 'mb_analysis'
mcmc_derive(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  modify_new_data = NULL,
  ref_data = FALSE,
  ref_fun2 = proportional_change2,
  random_effects = NULL,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)
## S3 method for class 'mb_analysis'
mcmc_derive(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  modify_new_data = NULL,
  ref_data = FALSE,
  ref_fun2 = proportional_change2,
  random_effects = NULL,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)

Arguments

`object`	An object inheriting from class mb_analysis.
`new_data`	The data frame to calculate the predictions for.
`new_expr`	A string of R code specifying the predictive relationship.
`new_values`	A named list of new or replacement values to pass to new_expr.
`term`	A string of the term in new_expr.
`modify_new_data`	A single argument function to modify new data (in list form) immediately prior to calculating new_expr.
`ref_data`	A flag or a data frame with 1 row indicating the reference values for calculating the effects size.
`ref_fun2`	A function whose first argument takes a vector of two numbers and returns a scalar of a metric of the difference between them.
`random_effects`	A named list specifying the random effects and the associated factors.
`new_expr_vec`	A flag specifying whether to vectorize the new_expr code.
`parallel`	A flag indicating whether to do predictions using parallel backend provided by foreach.
`quiet`	A flag indicating whether to disable tracing information.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Additional arguments.

Value

A object of class mcmcr.

MB Model

Description

Creates MB model.

Usage

model(
  x = NULL,
  ...,
  code = NULL,
  gen_inits = NULL,
  random_effects = list(),
  fixed = getOption("mb.fixed", "^[^e]"),
  derived = character(0),
  select_data = list(),
  center = character(0),
  scale = character(0),
  modify_data = identity,
  nthin = getOption("mb.nthin", 1L),
  new_expr = NULL,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  modify_new_data = identity,
  drops = list()
)
model(
  x = NULL,
  ...,
  code = NULL,
  gen_inits = NULL,
  random_effects = list(),
  fixed = getOption("mb.fixed", "^[^e]"),
  derived = character(0),
  select_data = list(),
  center = character(0),
  scale = character(0),
  modify_data = identity,
  nthin = getOption("mb.nthin", 1L),
  new_expr = NULL,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  modify_new_data = identity,
  drops = list()
)

Arguments

`x`	A string, or an object inheriting from class '"mb_code"'.
`...`	Unused arguments.
`code`	Passed on to [mb_code()]. If 'x' is not 'NULL', 'code' must be 'NULL', and vice versa.
`gen_inits`	A single argument function taking the modified data and returning a named list of initial values.
`random_effects`	A named list specifying the random effects and the associated factors.
`fixed`	A string of a regular expression specifying the fixed pars to monitor.
`derived`	A character vector of the derived pars to monitor.
`select_data`	A named list specifying the columns to select and their associated classes and values as well as transformations and scaling options.
`center`	A character vector of the columns to center.
`scale`	A character vector of the columns to scale (after centering).
`modify_data`	A single argument function to modify the data (in list form) immediately prior to the analysis.
`nthin`	A count specifying the thinning interval.
`new_expr`	A string of R code specifying the predictive relationships.
`new_expr_vec`	A flag specifying whether to vectorize the new_expr code.
`modify_new_data`	A single argument function to modify new data (in list form) immediately prior to calculating new_expr.
`drops`	A list of character vector of possible scalar pars to drop (fix at 0).

Details

For tmb models gen_inits must specify all the fixed pars. Missing random pars are assigned the value 0.

For jmb models unspecified the initial values for each chain are drawn from the prior distributions.

Value

An object inherting from class mb_model.

MB Models

Description

Creates an object inherting from class mb_models.

Usage

models(...)
models(...)

Arguments

...

Named objects.

Value

An object inheriting from class mb_models.

Modify Data

Description

Modifies a data frame to the form it will be passed to the analysis code.

Usage

modify_data(data, model, numericize_factors = FALSE)
modify_data(data, model, numericize_factors = FALSE)

Arguments

`data`	The data to modify.
`model`	An object inheriting from class mb_model.
`numericize_factors`	A flag indicating whether to convert factors to integer.

Value

The modified data in list form.

Modify New Data

Description

Modifies a data frame to the form it will be passed to the analysis code.

Usage

modify_new_data(
  data,
  data2,
  model,
  modify_new_data = NULL,
  numericize_factors = FALSE
)
modify_new_data(
  data,
  data2,
  model,
  modify_new_data = NULL,
  numericize_factors = FALSE
)

Arguments

`data`	The data to modify.
`data2`	The base data.
`model`	An object inheriting from class mb_model.
`modify_new_data`	A single argument function to modify new data (in list form) immediately prior to calculating new_expr.
`numericize_factors`	A flag indicating whether to convert factors to integer.

Value

The modified data in list form.

pars to monitor

Description

pars to monitor

Usage

monitor(object, param_type = "all")
monitor(object, param_type = "all")

Arguments

`object`	An mb model object to get the pars for.
`param_type`	A string specifying the type of pars to get.

Value

A character vector of the pars to monitor.

MB Analysis

Description

Constucts a new analysis object

Usage

new_analysis(x, class)
new_analysis(x, class)

Arguments

`x`	A list.
`class`	The class of the new object.

new_expr

Description

new_expr

Usage

new_expr(object, ...)
new_expr(object, ...)

Arguments

`object`	The object to get for.
`...`	Not used.

Value

The new_expr

new_expr set

Description

new_expr set

Usage

new_expr(object) <- value
new_expr(object) <- value

Arguments

`object`	The object to set for.
`value`	The new value of new expr.

Value

The modified object.

Total Number of MCMC simulations generated (including warmup)

Description

Total Number of MCMC simulations generated (including warmup)

Usage

ngens(x, ...)
ngens(x, ...)

Arguments

`x`	The object
`...`	Unused.

Value

A count.

Number Models

Description

Number Models

Usage

nmodels(x, ...)
nmodels(x, ...)

Arguments

`x`	the object.
`...`	Named objects.

Value

An integer of the number of models

Number of terms

Description

Number of terms

Usage

## S3 method for class 'mb_analysis'
nterms(x, param_type = "fixed", include_constant = TRUE, ...)
## S3 method for class 'mb_analysis'
nterms(x, param_type = "fixed", include_constant = TRUE, ...)

Arguments

`x`	The object to get the nterms for
`param_type`	A string indicating the type of terms to get the names for.
`include_constant`	A flag specifying whether to include constant terms.
`...`	unused

Thinning Rate

Description

Thinning Rate

Usage

nthin(x, ...)
nthin(x, ...)

Arguments

`x`	The object
`...`	Unused.

Value

A count.

Parameter Descriptions

Description

Parameter Descriptions

Arguments

`param_type`	A string indicating the type of terms to get the names for.
`type`	A string of the residual type.
`...`	Unused.

Plot Data

Description

Plot Data

Usage

plot_data(x, ...)
plot_data(x, ...)

Arguments

`x`	The object to plot the data for.
`...`	Unused.

Plot Residuals

Description

Plot Residuals

Usage

plot_residuals(x, ...)
plot_residuals(x, ...)

Arguments

`x`	The object to plot the residuals for.
`...`	Unused.

Plot Analysis

Description

Plot Analysis

Usage

## S3 method for class 'mb_analysis'
plot(x, param_type = "fixed", ...)
## S3 method for class 'mb_analysis'
plot(x, param_type = "fixed", ...)

Arguments

`x`	The analysis object to plot
`param_type`	A string indicating the type of terms to get the names for.
`...`	Unused.

Posterior Predictive Check

Description

Simulates

Usage

posterior_predictive_check(x, ...)
posterior_predictive_check(x, ...)

Arguments

`x`	The object
`...`	Unused.

Value

A tibble of the checks.

Posterior Predictive Check

Description

Posterior Predictive Check

Usage

## S3 method for class 'mb_analysis'
posterior_predictive_check(x, zeros = TRUE, ...)
## S3 method for class 'mb_analysis'
posterior_predictive_check(x, zeros = TRUE, ...)

Arguments

`x`	The MB analysis object.
`zeros`	A flag specifying whether to perform a posterior predictive check on the number of zeros in the data.
`...`	Unused.

Value

A tibble of the checks.

Predict

Description

Predict

Usage

## S3 method for class 'mb_analyses'
predict(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  conf_level = getOption("mb.conf_level", 0.95),
  modify_new_data = NULL,
  ref_data = FALSE,
  ref_fun2 = proportional_change2,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)
## S3 method for class 'mb_analyses'
predict(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  conf_level = getOption("mb.conf_level", 0.95),
  modify_new_data = NULL,
  ref_data = FALSE,
  ref_fun2 = proportional_change2,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)

Arguments

`object`	An object inheriting from class mb_analysis.
`new_data`	The data frame to calculate the predictions for.
`new_expr`	A string of R code specifying the predictive relationship.
`new_values`	A named list of new or replacement values to pass to new_expr.
`term`	A string of the term in new_expr.
`conf_level`	A number specifying the confidence level. By default 0.95.
`modify_new_data`	A single argument function to modify new data (in list form) immediately prior to calculating new_expr.
`ref_data`	A flag or a data frame with 1 row indicating the reference values for calculating the effects size.
`ref_fun2`	A function whose first argument takes a vector of two numbers and returns a scalar of a metric of the difference between them.
`new_expr_vec`	A flag specifying whether to vectorize the new_expr code.
`parallel`	A flag indicating whether to do predictions using parallel backend provided by foreach.
`quiet`	A flag indicating whether to disable tracing information.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Additional arguments.

Predict

Description

Predict

Usage

## S3 method for class 'mb_analysis'
predict(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  conf_level = getOption("mb.conf_level", 0.95),
  modify_new_data = NULL,
  ref_data = FALSE,
  ref_fun2 = proportional_change2,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  random_effects = NULL,
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)
## S3 method for class 'mb_analysis'
predict(
  object,
  new_data = data_set(object),
  new_expr = NULL,
  new_values = list(),
  term = "prediction",
  conf_level = getOption("mb.conf_level", 0.95),
  modify_new_data = NULL,
  ref_data = FALSE,
  ref_fun2 = proportional_change2,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  random_effects = NULL,
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)

Arguments

`object`	An object inheriting from class mb_analysis.
`new_data`	The data frame to calculate the predictions for.
`new_expr`	A string of R code specifying the predictive relationship.
`new_values`	A named list of new or replacement values to pass to new_expr.
`term`	A string of the term in new_expr.
`conf_level`	A number specifying the confidence level. By default 0.95.
`modify_new_data`	A single argument function to modify new data (in list form) immediately prior to calculating new_expr.
`ref_data`	A flag or a data frame with 1 row indicating the reference values for calculating the effects size.
`ref_fun2`	A function whose first argument takes a vector of two numbers and returns a scalar of a metric of the difference between them.
`new_expr_vec`	A flag specifying whether to vectorize the new_expr code.
`random_effects`	A named list specifying the random effects and the associated factors.
`parallel`	A flag indicating whether to do predictions using parallel backend provided by foreach.
`quiet`	A flag indicating whether to disable tracing information.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Additional arguments.

R2

Description

Gets the R2 value for an object.

Usage

R2(object, ...)
R2(object, ...)

Arguments

`object`	The object.
`...`	Unused.

Value

An index of the R2 value.

R2

Description

Gets the conditional (or marginal) R2 value for the 'response' for an mb_analysis object.

Usage

## S3 method for class 'mb_analysis'
R2(
  object,
  response,
  marginal = FALSE,
  term = "prediction",
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)
## S3 method for class 'mb_analysis'
R2(
  object,
  response,
  marginal = FALSE,
  term = "prediction",
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  beep = getOption("mb.beep", FALSE),
  ...
)

Arguments

`object`	The object.
`response`	A string specifying the column in the data corresponding to the response.
`marginal`	A flag indicating whether to calculate the marginal or conditional R2 value.
`term`	A string of the term in new_expr.
`parallel`	A flag indicating whether to do predictions using parallel backend provided by foreach.
`quiet`	A flag indicating whether to disable tracing information.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Unused

Details

The conditional R2 value is the proportion of the variance in the response predicted by the full model. The marginal R2 values is just for the fixed effects ie after marginalizing out the random effects.

Value

A number of the R2 value.

Random Effects

Description

Gets the random effects definitions for an object inheriting from class mb_model or mb_analysis.

Usage

random_effects(object, ...)
random_effects(object, ...)

Arguments

`object`	The object.
`...`	Unused.

Value

The random effects as a sorted named list.

Reanalyse

Description

Reanalyse an analysis.

Usage

reanalyse(object, ...)
reanalyse(object, ...)

Arguments

`object`	The object to reanalyse.
`...`	Additional arguments.

Reanalyse

Description

Reanalyse

Usage

## S3 method for class 'mb_analyses'
reanalyse(
  object,
  rhat = getOption("mb.rhat", 1.1),
  esr = getOption("mb.esr", 0.33),
  nreanalyses = getOption("mb.nreanalyses", 1L),
  duration = getOption("mb.duration", dhours(1)),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)
## S3 method for class 'mb_analyses'
reanalyse(
  object,
  rhat = getOption("mb.rhat", 1.1),
  esr = getOption("mb.esr", 0.33),
  nreanalyses = getOption("mb.nreanalyses", 1L),
  duration = getOption("mb.duration", dhours(1)),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)

Arguments

`object`	The object to reanalyse.
`rhat`	A number specifying the rhat threshold.
`esr`	A number specifying the effective sampling rate.
`nreanalyses`	A count between 1 and 7 specifying the maximum number of reanalyses.
`duration`	The maximum total time to spend on analysis/reanalysis.
`parallel`	A flag indicating whether to perform the analysis in parallel if possible
`quiet`	A flag indicating whether to disable tracing information.
`glance`	A flag indicating whether to print summary of model.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Unused arguments.

Reanalyse

Description

Reanalyse

Usage

## S3 method for class 'mb_analysis'
reanalyse(
  object,
  rhat = getOption("mb.rhat", 1.1),
  esr = getOption("mb.esr", 0.33),
  nreanalyses = getOption("mb.nreanalyses", 1L),
  duration = getOption("mb.duration", dhours(1)),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)
## S3 method for class 'mb_analysis'
reanalyse(
  object,
  rhat = getOption("mb.rhat", 1.1),
  esr = getOption("mb.esr", 0.33),
  nreanalyses = getOption("mb.nreanalyses", 1L),
  duration = getOption("mb.duration", dhours(1)),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)

Arguments

`object`	The object to reanalyse.
`rhat`	A number specifying the rhat threshold.
`esr`	A number specifying the minimum effective sampling rate.
`nreanalyses`	A count between 0 and 4 specifying the maximum number of reanalyses.
`duration`	The maximum total time to spend on analysis and reanalysis.
`parallel`	A flag indicating whether to perform the analysis in parallel if possible.
`quiet`	A flag indicating whether to disable tracing information.
`glance`	A flag indicating whether to print summary of model.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Unused arguments.

Reanalyse

Description

Reanalyse

Usage

## S3 method for class 'mb_meta_analyses'
reanalyse(
  object,
  rhat = getOption("mb.rhat", 1.1),
  esr = getOption("mb.esr", 0.33),
  nreanalyses = getOption("mb.nreanalyses", 1L),
  duration = getOption("mb.duration", dhours(1)),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)
## S3 method for class 'mb_meta_analyses'
reanalyse(
  object,
  rhat = getOption("mb.rhat", 1.1),
  esr = getOption("mb.esr", 0.33),
  nreanalyses = getOption("mb.nreanalyses", 1L),
  duration = getOption("mb.duration", dhours(1)),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)

Arguments

`object`	The object to reanalyse.
`rhat`	A number specifying the rhat threshold.
`esr`	A number specifying the effective sampling rate.
`nreanalyses`	A count between 1 and 7 specifying the maximum number of reanalyses.
`duration`	The maximum total time to spend on analysis/reanalysis.
`parallel`	A flag indicating whether to perform the analysis in parallel if possible
`quiet`	A flag indicating whether to disable tracing information.
`glance`	A flag indicating whether to print summary of model.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Unused arguments.

Reanalyse

Description

Reanalyse

Usage

## S3 method for class 'mb_meta_analysis'
reanalyse(
  object,
  rhat = getOption("mb.rhat", 1.1),
  esr = getOption("mb.esr", 0.33),
  nreanalyses = getOption("mb.nreanalyses", 1L),
  duration = getOption("mb.duration", dhours(1)),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)
## S3 method for class 'mb_meta_analysis'
reanalyse(
  object,
  rhat = getOption("mb.rhat", 1.1),
  esr = getOption("mb.esr", 0.33),
  nreanalyses = getOption("mb.nreanalyses", 1L),
  duration = getOption("mb.duration", dhours(1)),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)

Arguments

`object`	The object to reanalyse.
`rhat`	A number specifying the rhat threshold.
`esr`	A number specifying the effective sampling rate.
`nreanalyses`	A count between 1 and 7 specifying the maximum number of reanalyses.
`duration`	The maximum total time to spend on analysis/reanalysis.
`parallel`	A flag indicating whether to perform the analysis in parallel if possible
`quiet`	A flag indicating whether to disable tracing information.
`glance`	A flag indicating whether to print summary of model.
`beep`	A flag indicating whether to beep on completion of the analysis.
`...`	Unused arguments.

Reanalyse

Description

Reanalyse an analysis. For user to override.

Usage

reanalyse1(object, parallel, quiet, ...)
reanalyse1(object, parallel, quiet, ...)

Arguments

`object`	The object to reanalyse.
`parallel`	A flag indicating whether to perform the reanalysis in parallel.
`quiet`	A flag indicating whether to capture output.
`...`	Additional arguments.

Residuals

Description

Extract residual values for an MB analysis.

Usage

## S3 method for class 'mb_analysis'
residuals(object, type = NULL, ...)
## S3 method for class 'mb_analysis'
residuals(object, type = NULL, ...)

Arguments

`object`	The MB analysis object.
`type`	A string of the residual type.
`...`	Unused.

Details

The new_expr in the model must include the term 'residual'.

Value

The analysis data set with the residual values.

Removes comments from the template strings(s) of an mb object or a character vector.

Description

Removes comments from the template strings(s) of an mb object or a character vector.

Usage

rm_comments(object, ...)
rm_comments(object, ...)

Arguments

`object`	The mb object or character vector.
`...`	Unused.

Value

The mb object or character vector without comments in its template string(s).

Sample Size

Description

Get sample size.

Usage

sample_size(object, ...)
sample_size(object, ...)

Arguments

`object`	The object to calculate the sample size for.
`...`	Not used.

Value

A count of the sample size.

Scalar Named List

Description

Filters a named list so only scalar elements remain.wiby its names.

Usage

scalar_nlist(x)
scalar_nlist(x)

Arguments

`x`	The named list to sort.

Value

The sorted named list.

Examples

scalar_nlist(list(y = 2, x = 1, a = c(10, 1)))
scalar_nlist(list(y = 2, x = 1, a = c(10, 1)))

Multiply Standard Deviation of Priors By

Description

Multiply Standard Deviation of Priors By

Usage

sd_priors_by(x, by = 10, distributions = c("normal", "lognormal", "t"), ...)

## S3 method for class 'mb_model'
sd_priors_by(x, by = 10, distributions = c("normal", "lognormal", "t"), ...)

## S3 method for class 'mb_analysis'
sd_priors_by(
  x,
  by = 10,
  distributions = c("normal", "lognormal", "t"),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)
sd_priors_by(x, by = 10, distributions = c("normal", "lognormal", "t"), ...)

## S3 method for class 'mb_model'
sd_priors_by(x, by = 10, distributions = c("normal", "lognormal", "t"), ...)

## S3 method for class 'mb_analysis'
sd_priors_by(
  x,
  by = 10,
  distributions = c("normal", "lognormal", "t"),
  parallel = getOption("mb.parallel", FALSE),
  quiet = getOption("mb.quiet", TRUE),
  glance = getOption("mb.glance", TRUE),
  beep = getOption("mb.beep", TRUE),
  ...
)

Arguments

`x`	The object.
`by`	A double scalar of the multiplier.
`distributions`	A character vector of the distributions to adjust. Possible values are "laplace" (double exponential), "logistic", "lognormal", "normal", "t" and "nt" (non-central Student t).
`...`	Not used.
`parallel`	A flag indicating whether to perform the analysis in parallel if possible.
`quiet`	A flag indicating whether to disable tracing information.
`glance`	A flag indicating whether to print a model summary.
`beep`	A flag indicating whether to beep on completion of the analysis.

Value

The updated object.

Methods (by class)

sd_priors_by(mb_model): Multiply Standard Deviation of Priors for an MB model
sd_priors_by(mb_analysis): Multiply Standard Deviation of Priors for an MB analysis

Select and Rescale Data

Description

Selects and rescales data.

Usage

select_rescale_data(data, model, data2 = data)
select_rescale_data(data, model, data2 = data)

Arguments

`data`	The data to modify.
`model`	An object inheriting from class mb_model.
`data2`	The base data.

Value

The modified data in list form.

Set Analysis Mode

Description

Sets analysis mode.

Usage

set_analysis_mode(mode = "report")
set_analysis_mode(mode = "report")

Arguments

mode

A string of the analysis mode.

Details

The possible modes are as follows:

'debug': To rapidly identify problems with a model definition.
'quick': To quickly test code runs.
'report': To produce results for a report.
'paper': To produce results for a peer-reviewed paper.
'check': To run when checking a package.
'reset': To reset all the options to NULL so that they are the default values for each function call.

In each case the mode is a unique combination of the following package options

mb.nchains: A count of the number of chains.
mb.niters: A count of the number of simulations to save per chain.
mb.nthin: A count of the thining interval.
mb.parallel: A flag indicating whether to perform the analysis in parallel.
mb.quiet: A flag indicating whether to disable tracing information.
mb.beep: A flag indicating whether to beep on completion of the analysis.
mb.glance: A flag indicating whether to print a model summary.
mb.nreanalyses: A count specifying the maximum number of reanalyses.
mb.rhat: A number specifying the rhat threshold.
mb.esr: A number specifying the minimum effective sampling rate.
mb.duration: The maximum total time to spend on analysis and reanalysis.
mb.conf_level: A number specifying the confidence level.

Value

The old options.

Examples

## Not run: 
set_analysis_mode("reset")

## End(Not run)
## Not run: 
set_analysis_mode("reset")

## End(Not run)

Simulate Residuals

Description

Requires that new_expr includes 'residual <- res_bern(' or 'residual[i] <- res_norm('.

Usage

simulate_residuals(x, type = NULL)
simulate_residuals(x, type = NULL)

Arguments

`x`	The MB analysis object.
`type`	A string of the residual type.

Value

An mcmc_data of the simulated residuals.

Sort Analyses by IC

Description

Sort Analyses by IC

Usage

sort_by_ic(x, ...)
sort_by_ic(x, ...)

Arguments

`x`	the object.
`...`	Unused

Value

The sorted object

Sort Named List

Description

Sorts a named list by its names.

Usage

sort_nlist(x)
sort_nlist(x)

Arguments

`x`	The named list to sort.

Value

The sorted named list.

Examples

sort_nlist(list(y = 2, x = 1, a = 10))
sort_nlist(list(y = 2, x = 1, a = 10))

Template

Description

Gets the template string for an object.

Usage

template(object, ...)
template(object, ...)

Arguments

`object`	The object.
`...`	Additional arguments.

Value

The template model code as a string.

Set Template

Description

Sets the template for an object.

Usage

template(object) <- value
template(object) <- value

Arguments

`object`	The object.
`value`	A string of the new template

Terms

Description

terms

Usage

## S3 method for class 'mb_analysis'
terms(x, param_type = "fixed", include_constant = TRUE, ...)
## S3 method for class 'mb_analysis'
terms(x, param_type = "fixed", include_constant = TRUE, ...)

Arguments

`x`	The mb_analysis object.
`param_type`	A string indicating the type of terms to get the names for.
`include_constant`	A flag specifying whether to include constant terms.
`...`	Not used.

Update MB Model

Description

Updates an object inherting from class mb_model.

Usage

update_model(
  model,
  code = NULL,
  gen_inits = NULL,
  random_effects = NULL,
  fixed = NULL,
  derived = NULL,
  select_data = NULL,
  center = NULL,
  scale = NULL,
  modify_data = NULL,
  nthin = NULL,
  new_expr = NULL,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  modify_new_data = NULL,
  drops = NULL,
  ...
)
update_model(
  model,
  code = NULL,
  gen_inits = NULL,
  random_effects = NULL,
  fixed = NULL,
  derived = NULL,
  select_data = NULL,
  center = NULL,
  scale = NULL,
  modify_data = NULL,
  nthin = NULL,
  new_expr = NULL,
  new_expr_vec = getOption("mb.new_expr_vec", FALSE),
  modify_new_data = NULL,
  drops = NULL,
  ...
)

Arguments

`model`	The model to update.
`code`	A string of the model template or an object inheriting from class mb_code.
`gen_inits`	A single argument function taking the modified data and returning a named list of initial values.
`random_effects`	A named list specifying the random effects and the associated factors.
`fixed`	A string of a regular expression specifying the fixed pars to monitor.
`derived`	A character vector of the derived pars to monitor.
`select_data`	A named list specifying the columns to select and their associated classes and values as well as transformations and scaling options.
`center`	A character vector of the columns to center.
`scale`	A character vector of the columns to scale (after centering).
`modify_data`	A single argument function to modify the data (in list form) immediately prior to the analysis.
`nthin`	A count specifying the thinning interval.
`new_expr`	A string of R code specifying the predictive relationships.
`new_expr_vec`	A flag specifying whether to vectorize the new_expr code.
`modify_new_data`	A single argument function to modify new data (in list form) immediately prior to calculating new_expr.
`drops`	A list of character vector of possible scalar pars to drop (fix at 0).
`...`	Unused arguments.

Value

An object inheriting from class mb_model.

`x`	An mb_analysis or mb_analyses object.
`x2`	n mb_analysis or mb_analyses object.

`x`	An mb_model or mb_models object.
`x2`	n mb_model or mb_models object.

Package 'embr'

Help Index

Add analyses

Description

Usage

Arguments

Value

Add model(s)

Description

Usage

Arguments

Value

Analyse

Description

Usage

Arguments

Analyse Residuals

Description

Usage

Arguments

Analyse

Description

Usage

Arguments

Analyse

Description

Usage

Arguments

Analyse

Description

Usage

Arguments

Analyse

Description

Usage

Arguments

MB Analyses

Description

Usage

Arguments

Value

Coerce to an mb_analyses object

Description

Usage

Arguments

Coerce to an mb_model object

Description

Usage

Arguments

Coerce to an mb_models object

Description

Usage

Arguments

Backwards

Description

Usage

Arguments

Details

Value

Base Model

Description

Usage

Arguments

Value

Check MB Analysis

Description

Usage

Arguments

Value

Check MB Code

Description

Usage

Arguments

Value

Check MB Model

Description

Usage

Arguments

Value

Check Model Parameters