Package 'calmr' reference manual

Title:	Canonical Associative Learning Models and their Representations
Description:	Implementations of canonical associative learning models, with tools to run experiment simulations, estimate model parameters, and compare model representations. Experiments and results are represented using S4 classes and methods.
Authors:	Victor Navarro [aut, cre, cph]
Maintainer:	Victor Navarro <[email protected]>
License:	GPL (>= 3)
Version:	0.6.3
Built:	2025-03-04 04:34:19 UTC
Source:	https://github.com/victor-navarro/calmr

Create a graph with calmr data

Description

patch_graphs() patches graphs with 'patchwork'

Usage

calmr_model_graph(
  x,
  loops = TRUE,
  limits = max(abs(x$value)) * c(-1, 1),
  colour_key = FALSE,
  t = max(x$trial),
  options = get_graph_opts()
)

patch_graphs(graphs, selection = names(graphs))

get_graph_opts(graph_size = "small")
calmr_model_graph(
  x,
  loops = TRUE,
  limits = max(abs(x$value)) * c(-1, 1),
  colour_key = FALSE,
  t = max(x$trial),
  options = get_graph_opts()
)

patch_graphs(graphs, selection = names(graphs))

get_graph_opts(graph_size = "small")

Arguments

`x`	A `data.frame`-like with data to use in the plot. Contains a column named `value`.
`loops`	Logical. Whether to draw arrows back and forth
`limits`	Numerical. Limits for color scale. Defaults to max(abs(x$value))*c(-1,1).
`colour_key`	Logical. Whether to show the color key
`t`	The trial from which weights are obtained (defaults to the maximum trial in the data).
`options`	A list with graph options, as returned by `get_graph_opts()`.
`graphs`	A list of (named) graphs, as returned by `graph()` or `calmr_model_graph()`
`selection`	A character or numeric vector determining the plots to patch.
`graph_size`	A string (either "small" or "large"). to return default values for small or large graphs
`trial`	Numerical. The trial to graph.

Value

A 'ggplot' object

patch_graphs() returns a 'patchwork' object

A list with graph options, to be passed to ggnetwork::geom_nodes().

Note

You should probably be getting graphs via the graph method for CalmrExperiment.

Create a plot with calmr data

Description

plot_common_scale() rescales a list of plots to have a common scale.

get_plot_opts() returns generic plotting options.

patch_plots() patches plots using patchwork package.

Usage

calmr_model_plot(data, type, model, ...)

plot_common_scale(plots)

get_plot_opts(common_scale = TRUE)

patch_plots(plots, selection = names(plots), plot_options = get_plot_opts())
calmr_model_plot(data, type, model, ...)

plot_common_scale(plots)

get_plot_opts(common_scale = TRUE)

patch_plots(plots, selection = names(plots), plot_options = get_plot_opts())

Arguments

`data`	A `data.table` containing aggregated data from a CalmrExperiment
`type`	A character specifying the type of plot.
`model`	A character specifying the model.
`...`	Other parameters passed to plotting functions.
`plots`	A list of (named) plots, as returned by `plot()` or `calmr_model_plot()`
`common_scale`	Logical specifying whether to have plots in a common scale.
`selection`	A character or numeric vector determining the plots to patch
`plot_options`	A list of plot options as returned by `get_plot_opts()`

Value

A 'ggplot' object.

plot_common_scale() returns a list of plots.

get_plot_opts() returns a list.

patch_plots() returns a patchwork object.

Note

You should probably be getting plots via the plot() method for CalmrExperiment.

Set verbosity options for calmr

Description

Whether to show verbosity messages and progress bars

Usage

calmr_verbosity(verbose)
calmr_verbosity(verbose)

Arguments

verbose

A logical

Value

The list of progressr handlers (invisibly).

Note

Progress bars are handled by the progressr package. This is just a convenience function.

See package 'progressr' for further details.

S4 class for calmr designs

Description

S4 class for calmr designs

Slots

design:: A list containing design information.
mapping:: A list containing the object mapping.
raw_design:: The original data.frame.

CalmrDesign methods

Description

S4 methods for CalmrDesign class.

Usage

## S4 method for signature 'CalmrDesign'
show(object)

## S4 method for signature 'CalmrDesign'
mapping(object)

## S4 method for signature 'CalmrDesign'
trials(object)
## S4 method for signature 'CalmrDesign'
show(object)

## S4 method for signature 'CalmrDesign'
mapping(object)

## S4 method for signature 'CalmrDesign'
trials(object)

Arguments

object

A CalmrDesign object

Value

show() returns NULL (invisibly).

mapping() returns a list with trial mappings.

trials() returns NULL (invisibly).

S4 class for calmr experiments.

Description

S4 class for calmr experiments.

Slots

design:: A CalmrDesign object.
model:: A string specifying the model used.
groups:: A string specifying the groups in the design.
parameters:: A list with the parameters used, per group.
timings:: A list with the timings used in the design.
experiences:: A list with the experiences for the model.
results:: A CalmrExperimentResult object.
.model:: Internal. The model associated with the iteration.
.group:: Internal. The group associated with the iteration.
.iter:: Internal. The iteration number.

CalmrExperiment methods

Description

S4 methods for CalmrExperiment class.

Usage

## S4 method for signature 'CalmrExperiment'
show(object)

## S4 method for signature 'CalmrExperiment'
design(x)

## S4 method for signature 'CalmrExperiment'
trials(object)

## S4 method for signature 'CalmrExperiment'
parameters(x)

## S4 replacement method for signature 'CalmrExperiment'
parameters(x) <- value

## S4 method for signature 'CalmrExperiment'
experiences(x)

## S4 replacement method for signature 'CalmrExperiment'
experiences(x) <- value

## S4 method for signature 'CalmrExperiment'
results(object)

## S4 method for signature 'CalmrExperiment'
raw_results(object)

## S4 method for signature 'CalmrExperiment'
parsed_results(object)

## S4 method for signature 'CalmrExperiment'
length(x)

## S4 method for signature 'CalmrExperiment'
parse(object, outputs = NULL)

## S4 method for signature 'CalmrExperiment'
aggregate(x, outputs = NULL)

## S4 method for signature 'CalmrExperiment'
plot(x, type = NULL, ...)

## S4 method for signature 'CalmrExperiment'
graph(x, ...)

## S4 method for signature 'CalmrExperiment'
timings(x)

## S4 replacement method for signature 'CalmrExperiment'
timings(x) <- value
## S4 method for signature 'CalmrExperiment'
show(object)

## S4 method for signature 'CalmrExperiment'
design(x)

## S4 method for signature 'CalmrExperiment'
trials(object)

## S4 method for signature 'CalmrExperiment'
parameters(x)

## S4 replacement method for signature 'CalmrExperiment'
parameters(x) <- value

## S4 method for signature 'CalmrExperiment'
experiences(x)

## S4 replacement method for signature 'CalmrExperiment'
experiences(x) <- value

## S4 method for signature 'CalmrExperiment'
results(object)

## S4 method for signature 'CalmrExperiment'
raw_results(object)

## S4 method for signature 'CalmrExperiment'
parsed_results(object)

## S4 method for signature 'CalmrExperiment'
length(x)

## S4 method for signature 'CalmrExperiment'
parse(object, outputs = NULL)

## S4 method for signature 'CalmrExperiment'
aggregate(x, outputs = NULL)

## S4 method for signature 'CalmrExperiment'
plot(x, type = NULL, ...)

## S4 method for signature 'CalmrExperiment'
graph(x, ...)

## S4 method for signature 'CalmrExperiment'
timings(x)

## S4 replacement method for signature 'CalmrExperiment'
timings(x) <- value

Arguments

`object`, `x`	A `CalmrExperiment` object.
`value`	A list of parameters (or list of parameter lists).
`outputs`	A character vector specifying the model outputs to parse.
`type`	A character vector specifying the type(s) of plots to create. Defaults to NULL. See supported_plots.
`...`	Extra arguments passed to `calmr_model_graph()` and `calmr_model_plot()`.

Value

show() returns NULL (invisibly).

design() returns the CalmrDesign contained in the object.

trials() returns NULL (invisibly).

parameters() returns the list of parameters contained in the object.

⁠parameters()<-⁠ returns the object after updating parameters.

experiences() returns a list of data.frame objects containing model training routines.

⁠experiences()<-⁠ returns the object after updating experiences.

results() returns a data.table objects with aggregated results.

raw_results() returns a list with raw model results.

parsed_results() returns a list of data.table objects with parsed results.

length() returns an integer specifying the total length of the experiment (groups by iterations).

parse() returns the object after parsing raw results.

aggregate() returns the object after aggregating parsed results.

plot() returns a list of 'ggplot' plot objects.

graph() returns a list of 'ggplot' plot objects.

timings() returns the list of timings contained in the object.

⁠timings()<-⁠ returns the object after updating timings.

S4 class for calmr experiment results

Description

S4 class for calmr experiment results

Slots

aggregated_results: A list of data.table objects with aggregated results.
parsed_results: A list containing data.table objects with parsed results.
raw_results: A list with raw model outputs.

S4 class for calmr Fit

Description

S4 class for calmr Fit

Slots

nloglik:: Numeric. Negative log likelihood of the fit
best_pars:: Numeric. Best fitting parameters
model_pars:: Numeric. Parameters used in the model function
link_pars:: Numeric. Parameters used in the link function
data:: Numeric. Data used for fit
model_function:: Function. Model function
link_function:: Function. Link function
ll_function:: Function. Objective function (usually nloglikelihood)
optimizer_options:: List. Options used for the optimizer
extra_pars:: List. Extra parameters passed to the fit call (...)

CalmrFit methods

Description

S4 methods for CalmrFit class.

Usage

## S4 method for signature 'CalmrFit'
show(object)

## S4 method for signature 'CalmrFit'
predict(object, type = "response", ...)

## S4 method for signature 'CalmrFit'
NLL(object)

## S4 method for signature 'CalmrFit'
AIC(object, k = 2)

## S4 method for signature 'CalmrFit'
BIC(object)
## S4 method for signature 'CalmrFit'
show(object)

## S4 method for signature 'CalmrFit'
predict(object, type = "response", ...)

## S4 method for signature 'CalmrFit'
NLL(object)

## S4 method for signature 'CalmrFit'
AIC(object, k = 2)

## S4 method for signature 'CalmrFit'
BIC(object)

Arguments

`object`	A `CalmrFit` object.
`type`	A string specifying the type of prediction to generate.
`...`	Extra named arguments.
`k`	Penalty term for `AIC` method.

Details

With type = "response", the predict() function passed model responses to the link function used to fit the model.

The AIC is defined as 2*k - 2*-NLL, where k is a penalty term and NLL is the negative log likelihood of the model.

The BIC is defined as p*log(n) - 2*-NLL, where p is the number of parameters in the model and n is the number of observations

Value

show() returns NULL (invisibly).
predict() returns a numeric vector.
NLL() returns the negative log likelihood of the model.
AIC() returns the Akaike Information Criterion (AIC) of the model.
BIC() returns the Bayesian Information Criterion (BIC) of the model.

S4 class for calmr results

Description

S4 class for calmr results

Slots

aggregated_results: A list of data.table objects with aggregated results.
parsed_results: A list containing data.table objects with parsed results.
raw_results: A list with raw model outputs.

CalmrResult methods

Description

S4 methods for CalmrResults class.

Usage

## S4 method for signature 'CalmrResult'
show(object)
## S4 method for signature 'CalmrResult'
show(object)

Arguments

object

A CalmrResults object.

Value

show() returns NULL (invisibly).

S4 class for calmr representational similarity analysis

Description

S4 class for calmr representational similarity analysis

Slots

corr_mat:: An array containing the correlation matrix
distances:: A list of pairwise distance matrices
args:: A list of the arguments used to create the object.
test_data:: A list with permutation data, only populated after testing the object.

CalmrRSA methods

Description

S4 methods for CalmrRSA class.

Usage

## S4 method for signature 'CalmrRSA'
show(object)

## S4 method for signature 'CalmrRSA'
test(object, n_samples = 1000, p = 0.95)

## S4 method for signature 'CalmrRSA'
plot(x)
## S4 method for signature 'CalmrRSA'
show(object)

## S4 method for signature 'CalmrRSA'
test(object, n_samples = 1000, p = 0.95)

## S4 method for signature 'CalmrRSA'
plot(x)

Arguments

`object`, `x`	A `CalmrRSA` object.
`n_samples`	The number of samples for the permutation test (default = 1e3)
`p`	The critical threshold level for the permutation test (default = 0.95)

Value

show() returns NULL (invisibly).
test() returns the CalmrRSA object with permutation test data.
plot() returns a list of 'ggplot' plot objects.

Run models given a set of parameters

Description

Run models given a set of parameters

Usage

compare_models(x, models = NULL, ...)
compare_models(x, models = NULL, ...)

Arguments

`x`	A list of CalmrExperiment objects or a design data.frame.
`models`	A character vector of length m, specifying the models to run. Ignored if x is a list of CalmrExperiment objects.
`...`	Arguments passed to make_experiment.

Value

A list of CalmrExperiment objects

Examples

# By making experiment beforehand (recommended)
df <- get_design("blocking")
models <- c("HD2022", "RW1972", "PKH1982")
exps <- lapply(models, function(m) {
  make_experiment(df,
    parameters = get_parameters(df, model = m),
    model = m
  )
})
comp <- compare_models(exps)

# By passing minimal arguments (not recommended; default parameters)
comp <- compare_models(df, models = models)
# By making experiment beforehand (recommended)
df <- get_design("blocking")
models <- c("HD2022", "RW1972", "PKH1982")
exps <- lapply(models, function(m) {
  make_experiment(df,
    parameters = get_parameters(df, model = m),
    model = m
  )
})
comp <- compare_models(exps)

# By passing minimal arguments (not recommended; default parameters)
comp <- compare_models(df, models = models)

Fit model to data

Description

Obtain MLE estimates for model, given data.

Usage

fit_model(data, model_function, optimizer_options, file = NULL, ...)
fit_model(data, model_function, optimizer_options, file = NULL, ...)

Arguments

`data`	A numeric vector containing data to fit model against.
`model_function`	A function that runs the model and returns data.frame of value, organized as in `data`.
`optimizer_options`	A list with options for the optimizer, as returned by get_optimizer_opts.
`file`	A path to save the model fit. If the arguments to the fit call are found to be identical to those in the file, the model just gets loaded.
`...`	Extra parameters passed to the optimizer call.

Value

A CalmrFit object

Note

See the calmr_fits vignette for examples

Examples

# Make some fake data
df <- data.frame(g = "g", p1 = "3A>(US)", r1 = TRUE)
pars <- get_parameters(df, model = "RW1972")
pars$alphas["US"] <- 0.9
exper <- make_experiment(df, parameters = pars, model = "RW1972")
res <- run_experiment(exper, outputs = "responses")
responses <- results(res)$responses$value

# define model function
model_fun <- function(p, ex) {
  np <- parameters(ex)
  np[[1]]$alphas[] <- p
  parameters(ex) <- np
  results(run_experiment(ex))$responses$value
}

# Get optimizer options
optim_opts <- get_optimizer_opts(
  model_pars = names(pars$alphas),
  ll = rep(.05, 2), ul = rep(.95, 2),
  optimizer = "optim", family = "identity"
)
optim_opts$initial_pars[] <- rep(.6, 2)

fit_model(responses, model_fun, optim_opts,
  ex = exper, method = "L-BFGS-B",
  control = list(maxit = 1)
)
# Make some fake data
df <- data.frame(g = "g", p1 = "3A>(US)", r1 = TRUE)
pars <- get_parameters(df, model = "RW1972")
pars$alphas["US"] <- 0.9
exper <- make_experiment(df, parameters = pars, model = "RW1972")
res <- run_experiment(exper, outputs = "responses")
responses <- results(res)$responses$value

# define model function
model_fun <- function(p, ex) {
  np <- parameters(ex)
  np[[1]]$alphas[] <- p
  parameters(ex) <- np
  results(run_experiment(ex))$responses$value
}

# Get optimizer options
optim_opts <- get_optimizer_opts(
  model_pars = names(pars$alphas),
  ll = rep(.05, 2), ul = rep(.95, 2),
  optimizer = "optim", family = "identity"
)
optim_opts$initial_pars[] <- rep(.6, 2)

fit_model(responses, model_fun, optim_opts,
  ex = exper, method = "L-BFGS-B",
  control = list(maxit = 1)
)

Get basic designs

Description

Get basic designs

Usage

get_design(design_name = NULL)
get_design(design_name = NULL)

Arguments

design_name

A string specifying a design name (default = NULL)

Value

If design_name is not NULL, a data.frame containing the design. Otherwise, a list containing all available designs.

Examples

names(get_design())
get_design("blocking")
names(get_design())
get_design("blocking")

Get optimizer options

Description

Get optimizer options

Usage

get_optimizer_opts(
  model_pars,
  initial_pars = rep(NA, length(model_pars)),
  ll = rep(NA, length(model_pars)),
  ul = rep(NA, length(model_pars)),
  optimizer = NULL,
  family = NULL
)
get_optimizer_opts(
  model_pars,
  initial_pars = rep(NA, length(model_pars)),
  ll = rep(NA, length(model_pars)),
  ul = rep(NA, length(model_pars)),
  optimizer = NULL,
  family = NULL
)

Arguments

`model_pars`	A character vector specifying the name of the parameters to fit.
`initial_pars`	A numeric vector specifying the initial parameter values to #' evaluate the model at (required by `optim`). Defaults to 0 for each parameter.
`ll`, `ul`	A numeric vector specifying the lower and upper limits of the parameters to fit, respectively
`optimizer`	A string specifying the optimizer to use. One from `c("optim", "ga")`
`family`	A string specifying the family function to generate responses (and calculate the likelihood function with). One from `c("identity", "normal", "poisson")`.

Value

A list with optimizer options.

Note

Whenever a family function other than the identity is used, the family-specific parameters will always be appended to the end of the relevant lists.

Get model parameters

Description

Get model parameters

Usage

get_parameters(design, model = NULL)
get_parameters(design, model = NULL)

Arguments

`design`	A `data.frame` containing the experimental design.
`model`	A string specifying a model. One in `supported_models()`.

Value

A list with model parameters depending on model

Examples

block <- get_design("blocking")
get_parameters(block, model = "SM2007")
block <- get_design("blocking")
get_parameters(block, model = "SM2007")

Get timing design parameters

Description

Get timing design parameters

Usage

get_timings(design, model)
get_timings(design, model)

Arguments

`design`	A `data.frame` containing the experimental design.
`model`	One of `supported_timed_models()`.

Value

A list of timing design parameters.

Examples

block <- get_design("blocking")
get_timings(block, model = "TD")
block <- get_design("blocking")
get_timings(block, model = "TD")

The miniblocks option will direct the sampling function to create equally-sized miniblocks with random trials within a phase. For example, the phase string "2A/2B" will create two miniblocks with one of each trial. The phase string "2A/4B" will create two miniblocks with one A trial, and 2 B trials. However, the phase string "2A/1B" will not result in miniblocks, even if miniblocks here is set to TRUE.

Examples

des <- data.frame(Group = "G1", P1 = "10A>(US)", R1 = TRUE)
ps <- get_parameters(des, model = "HD2022")
make_experiment(
  design = des, parameters = ps,
  model = "HD2022", iterations = 2
)
des <- data.frame(Group = "G1", P1 = "10A>(US)", R1 = TRUE)
ps <- get_parameters(des, model = "HD2022")
make_experiment(
  design = des, parameters = ps,
  model = "HD2022", iterations = 2
)

Model information functions

Description

An assortment of functions to return model information.

Usage

supported_models()

supported_timed_models()

supported_optimizers()

supported_families()

supported_plots(model = NULL)

get_model(model)

model_parameters(model = NULL)

model_outputs(model = NULL)
supported_models()

supported_timed_models()

supported_optimizers()

supported_families()

supported_plots(model = NULL)

get_model(model)

model_parameters(model = NULL)

model_outputs(model = NULL)

Arguments

model

A string specifying a model. One from supported_models().

Value

supported_models() returns a character vector.

supported_timed_models() returns a character vector.

supported_optimizers() returns a character vector.

supported_families() returns a character vector.

supported_plots() returns a character vector or list (if model is NULL).

get_model() returns a model function.

model_parameters() returns a list or a list of lists (if model is NULL).

model_outputs() returns a character vector or list (if model is NULL).

Examples

# Outputs and plots supported by the RW1972 model
model_outputs("RW1972")

# Getting the model function implementing the PKH1982 model
pkh_func <- get_model("PKH1982")
head(pkh_func, 10)

# Getting the parameters required by SM2007
model_parameters("SM2007")
# Outputs and plots supported by the RW1972 model
model_outputs("RW1972")

# Getting the model function implementing the PKH1982 model
pkh_func <- get_model("PKH1982")
head(pkh_func, 10)

# Getting the parameters required by SM2007
model_parameters("SM2007")

Parse design data.frame

Description

Parse design data.frame

Usage

parse_design(df)
parse_design(df)

Arguments

`df`	A `data.frame` of dimensions (groups) by (2*phases+1).

Value

A CalmrDesign object.

Note

Each entry in even-numbered columns of df is a string formatted as per phase_parser().

Examples

df <- data.frame(
  Group = c("Group 1", "Group 2"),
  P1 = c("10AB(US)", "10A(US)"), R1 = c(TRUE, TRUE)
)
parse_design(df)
df <- data.frame(
  Group = c("Group 1", "Group 2"),
  P1 = c("10AB(US)", "10A(US)"), R1 = c(TRUE, TRUE)
)
parse_design(df)

Rat responses from Patittucci et al. 2016

Description

A dataset containing rat nose pokes and lever presses when levers were associated with different appetitive stimuli.

Usage

pati
pati

Format

A data.frame with the following variables:

subject: subject identifier
block: the 2-session block of training (1 to 8)
lever: lever presented on the trial: L = left; R = right
us: the stimulus that followed the lever: P = pellet; S = sucrose
response: the response: lp = lever press; np = nose poke
rpert: responses per trial

...

Source

Patittucci et al. (2016). JEP:ALC

Parses a phase string

Description

Parses a phase string

Usage

phase_parser(phase_string)
phase_parser(phase_string)

Arguments

phase_string

A string specifying trials within a phase.

Value

A named list with:

trial_info:: A trial-named list of lists.
general_info:: General phase information.

Note

This function is meant for internal use only, but we expose it so you can test your strings.

Examples

# A silly (but valid) string
phase_parser("10#Rescorla>Wagner")

# An invalid string that needs trial repetitions for one of trials.
try(phase_parser("10#Rescorla/Wagner"))
# A silly (but valid) string
phase_parser("10#Rescorla>Wagner")

# An invalid string that needs trial repetitions for one of trials.
try(phase_parser("10#Rescorla/Wagner"))

General plotting functions

Description

plot_targetted_tbins() plots targetted time data on a trial.

plot_tbins() plots non-targetted time data on a trial.

plot_targetted_trials() plots targetted trial data.

plot_trials() plots non-targetted trial data.

plot_targetted_typed_trials() plots targetted trial data with a type.

plot_targetted_complex_trials() plots targetted data with a third variable.

Usage

plot_targetted_tbins(data, t = max(data$trial))

plot_tbins(data, t = max(data$trial))

plot_targetted_trials(data)

plot_trials(data)

plot_targetted_typed_trials(data)

plot_targetted_complex_trials(data, col)
plot_targetted_tbins(data, t = max(data$trial))

plot_tbins(data, t = max(data$trial))

plot_targetted_trials(data)

plot_trials(data)

plot_targetted_typed_trials(data)

plot_targetted_complex_trials(data, col)

Arguments

`data`	A `data.frame`-like with data to plot.
`t`	A numeric vector specifying the trial(s) to plot. Defaults to the last trial in data.
`col`	A string specifying the column of the third variable.

Value

plot_targetted_tbins() returns 'ggplot' object.

plot_tbins() returns 'ggplot' object.

plot_targetted_trials() returns 'ggplot' object.

plot_trials() returns 'ggplot' object.

plot_targetted_typed_trials() returns 'ggplot' object.

plot_targetted_complex_trials() returns 'ggplot' object.

Note

All data must be organised as returned by results() or parsed_results().

Perform representational similarity analysis

Description

Perform representational similarity analysis

Usage

rsa(x, comparisons, test = FALSE, ...)
rsa(x, comparisons, test = FALSE, ...)

Arguments

`x`	A list of CalmrExperiment objects
`comparisons`	A model-named list containing the model outputs to compare.
`test`	Whether to test the RSA via permutation test. Default = FALSE.
`...`	Additional parameters passed to `stats::dist()` and `stats::cor()`

Value

A CalmrRSA object

Note

The object returned by this function can be later tested via its own test() method.

Examples

# Comparing the associations in three models
exp <- data.frame(
  Group = c("A", "B"),
  P1 = c("2(A)>(US)/1B>(US)", "1(A)>(US)/2B>(US)"),
  R1 = TRUE
)
models <- c("HD2022", "RW1972", "PKH1982")
parameters <- sapply(models, get_parameters, design = exp)
exp_res <- compare_models(exp,
  models = models
)
comparisons <- list(
  "HD2022" = c("associations"),
  "RW1972" = c("associations"),
  "PKH1982" = c("associations")
)
res <- rsa(exp_res, comparisons = comparisons)
test(res, n_samples = 20)
# Comparing the associations in three models
exp <- data.frame(
  Group = c("A", "B"),
  P1 = c("2(A)>(US)/1B>(US)", "1(A)>(US)/2B>(US)"),
  R1 = TRUE
)
models <- c("HD2022", "RW1972", "PKH1982")
parameters <- sapply(models, get_parameters, design = exp)
exp_res <- compare_models(exp,
  models = models
)
comparisons <- list(
  "HD2022" = c("associations"),
  "RW1972" = c("associations"),
  "PKH1982" = c("associations")
)
res <- rsa(exp_res, comparisons = comparisons)
test(res, n_samples = 20)

Run experiment

Description

Runs an experiment with minimal parameters.

Usage

run_experiment(x, outputs = NULL, parse = TRUE, aggregate = TRUE, ...)
run_experiment(x, outputs = NULL, parse = TRUE, aggregate = TRUE, ...)

Arguments

`x`	A CalmrExperiment or design `data.frame`
`outputs`	A character vector specifying which outputs to parse and aggregate. Defaults to NULL, in which case all model outputs are parsed/aggregated.
`parse`	A logical specifying whether the raw results should be parsed. Default = TRUE.
`aggregate`	A logical specifying whether the parsed results should be aggregated. Default = TRUE.
`...`	Arguments passed to other functions

Value

A CalmrExperiment with results.

Examples

# Using a data.frame only (throws warning)
df <- get_design("relative_validity")
run_experiment(df, model = "RW1972")

# Using custom parameters
df <- get_design("relative_validity")
pars <- get_parameters(df, model = "HD2022")
pars$alphas["US"] <- 0.6
run_experiment(df, parameters = pars, model = "HD2022")

# Using make_experiment, for more iterations
df <- get_design("blocking")
pars <- get_parameters(df, model = "SM2007")
exper <- make_experiment(df,
  parameters = pars, model = "SM2007",
  iterations = 4
)
run_experiment(exper)

# Only parsing the associations in the model, without aggregation
run_experiment(exper, outputs = "associations", aggregate = FALSE)
# Using a data.frame only (throws warning)
df <- get_design("relative_validity")
run_experiment(df, model = "RW1972")

# Using custom parameters
df <- get_design("relative_validity")
pars <- get_parameters(df, model = "HD2022")
pars$alphas["US"] <- 0.6
run_experiment(df, parameters = pars, model = "HD2022")

# Using make_experiment, for more iterations
df <- get_design("blocking")
pars <- get_parameters(df, model = "SM2007")
exper <- make_experiment(df,
  parameters = pars, model = "SM2007",
  iterations = 4
)
run_experiment(exper)

# Only parsing the associations in the model, without aggregation
run_experiment(exper, outputs = "associations", aggregate = FALSE)

Get/set the colour/fill palette for plots

Description

Get/set the colour/fill palette for plots

Usage

set_calmr_palette(palette = NULL)
set_calmr_palette(palette = NULL)

Arguments

palette

A string specifying the available palettes. If NULL, returns available palettes.

Value

The old palette (invisibly) if palette is not NULL. Otherwise, a character vector of available palettes.

Note

Changes here do not affect the palette used in graphs.

Set reward parameters for ANCCR model

Description

Set reward parameters for ANCCR model

Usage

set_reward_parameters(parameters, rewards = c("US"))
set_reward_parameters(parameters, rewards = c("US"))

Arguments

`parameters`	A list of parameters, as returned by `get_parameters()`
`rewards`	A character vector specifying the reward stimuli. Default = `c("US")`

Value

A list of parameters

Note

The default behaviour of get_parameters for the ANCCR model is to set every reward-related parameter to its non-zero default value. This function will set those parameters to zero for non-reward stimuli

`design`	A design `data.frame`.
`model`	A string specifying the model name. One of `supported_models()`.
`parameters`	Optional. Parameters for a model as returned by `get_parameters()`.
`timings`	Optional. Timings for a time-based design as returned by `get_timings()`
`iterations`	An integer specifying the number of iterations per group. Default = 1.
`miniblocks`	Whether to organize trials in miniblocks. Default = TRUE.
`.callback_fn`	A function for keeping track of progress. Internal use.
`...`	Extra parameters passed to other functions.

Package 'calmr'

Help Index

Create a graph with calmr data

Description

Usage

Arguments

Value

Note

Create a plot with calmr data

Description

Usage

Arguments

Value

Note

See Also

Set verbosity options for calmr

Description

Usage

Arguments

Value

Note

S4 class for calmr designs

Description

Slots

CalmrDesign methods

Description

Usage

Arguments

Value

S4 class for calmr experiments.

Description

Slots

See Also

CalmrExperiment methods

Description

Usage

Arguments

Value

See Also

S4 class for calmr experiment results

Description

Slots

S4 class for calmr Fit

Description

Slots

See Also

CalmrFit methods

Description

Usage

Arguments

Details

Value

S4 class for calmr results

Description

Slots

See Also

CalmrResult methods

Description

Usage

Arguments

Value

S4 class for calmr representational similarity analysis

Description

Slots

CalmrRSA methods

Description

Usage

Arguments

Value

Run models given a set of parameters

Description

Usage

Arguments

Value

Examples

Fit model to data

Description

Usage

Arguments

Value