,
Jeffrey Bye [ctb]| Title: | Flexible 'Tidyverse'-Friendly Simulations |
|---|---|
| Description: | A general, 'tidyverse'-friendly framework for simulation studies, design analysis, and power analysis. Specify data generation, define varying parameters, generate data, fit models, and tidy model results in a single pipeline, without needing loops or custom functions. |
| Authors: | Ethan Brown [aut, cre] ,
Jeffrey Bye [ctb] |
| Maintainer: | Ethan Brown <[email protected]> |
| License: | GPL-2 |
| Version: | 0.2.6 |
| Built: | 2025-02-12 05:42:30 UTC |
| Source: | https://github.com/statisfactions/simpr |
This function applies a given function to all
fit objects and returns the result in a tidy
tibble. Any function or purrr-style
lambda function can be used.
apply_fits(obj, .f, ..., .progress = FALSE, .options = furrr_options())apply_fits(obj, .f, ..., .progress = FALSE, .options = furrr_options())
obj |
a |
.f |
A function or |
... |
Additional arguments to |
.progress |
A logical, for whether or not to print a progress bar for multiprocess, multisession, and multicore plans . |
.options |
The |
A tibble with columns .sim_id,
rep, Source (which contains the name of the
fit column), any metaparameters from
define, and additional columns
containing the results of .f applied to each fit
object.
set.seed(100) logit_fit = specify(a = ~ sample(0:1, size = n, replace = TRUE), b = ~ a + rlnorm(n)) %>% define(n = c(40, 50)) %>% generate(1) %>% fit(logit = ~ glm(a ~ b, family = "binomial")) logit_fit %>% apply_fits(broom::augment) ## Arguments to the function can be passed in ... logit_fit %>% apply_fits(broom::augment, se_fit = TRUE) ## Using a purrr-style lambda function logit_fit %>% apply_fits(~ summary(.)$cov.scaled)set.seed(100) logit_fit = specify(a = ~ sample(0:1, size = n, replace = TRUE), b = ~ a + rlnorm(n)) %>% define(n = c(40, 50)) %>% generate(1) %>% fit(logit = ~ glm(a ~ b, family = "binomial")) logit_fit %>% apply_fits(broom::augment) ## Arguments to the function can be passed in ... logit_fit %>% apply_fits(broom::augment, se_fit = TRUE) ## Using a purrr-style lambda function logit_fit %>% apply_fits(~ summary(.)$cov.scaled)
Takes the output of
specify (a
simpr_spec object) and defines the
metaparameters (i.e. simulation factors).
define(.x = NULL, ..., .list = NULL, .suffix = "_index")define(.x = NULL, ..., .list = NULL, .suffix = "_index")
.x |
a |
... |
metaparameters: named arguments containing vectors or lists of objects to be used in the simulation. |
.list |
additional parameters passed to define() as a list. Useful if you already have desired metaparameters already in list format or created by other functions. |
.suffix |
name of suffix to append onto
index column for list metaparameters,
|
This is the second step in the simulation
process, after specifying the simulated data
using specify.
The output of define is then
passed to
generate to
actually generate the simulation.
Metaparameters are named arguments, passed to
..., that are used in the simulation.
A metaparameter is some kind of vector or list,
representing something that is to be
systematically varied as a part of the
simulation design. Any metaparameter should
also appear in the formulas of
specify, and
thus the simulation changes depending on the
value of the metaparameter.
When creating the simulation, simulations for
all possible combinations of metaparameters are
generated, resulting in a fully crossed
simulation design. If only a subset of the
fully crossed design is needed, use the
filtering options available in
generate.
When one of ... is a list, a new
column is generated in the output to
generate to
serve as the index of the list. This new
column will be the name of the list argument,
with the suffix argument appended onto
the end. So if Y = list(a = 1:2, b =
letters[2:3]), and suffix = "_index",
the default, a column named Y_index
would be added to the output of
generate
with values "a" and "b".
a simpr_spec object to pass onto
generate
for the simulation.
# Simple example of setting a metaparameter simple_meta = specify(a = ~ 1 + rnorm(n)) %>% define(n = c(5, 10)) %>% generate(1) simple_meta # $sim has a 5-row tibble and a 10-row tibble multi_meta = specify(a = ~ mu + rnorm(n)) %>% define(n = c(5, 10), mu = seq(-1, 1, length.out = 3)) %>% generate(1) multi_meta # generates simulations for all combos of n and mu # define can handle lists which can contain multiple matrices, etc. meta_list_out = specify(a = ~ MASS::mvrnorm(n, rep(0, 2), Sigma = S)) %>% define(n = c(10, 20, 30), S = list(independent = diag(2), correlated = diag(2) + 2)) %>% generate(1) meta_list_out # generates S_index column # define can also take arguments as a list using the .list argument meta_list_out_2 = specify(a = ~ MASS::mvrnorm(n, rep(0, 2), Sigma = S)) %>% define(.list = list(n = c(10, 20, 30), S = list(independent = diag(2), correlated = diag(2) + 2))) %>% generate(1)# Simple example of setting a metaparameter simple_meta = specify(a = ~ 1 + rnorm(n)) %>% define(n = c(5, 10)) %>% generate(1) simple_meta # $sim has a 5-row tibble and a 10-row tibble multi_meta = specify(a = ~ mu + rnorm(n)) %>% define(n = c(5, 10), mu = seq(-1, 1, length.out = 3)) %>% generate(1) multi_meta # generates simulations for all combos of n and mu # define can handle lists which can contain multiple matrices, etc. meta_list_out = specify(a = ~ MASS::mvrnorm(n, rep(0, 2), Sigma = S)) %>% define(n = c(10, 20, 30), S = list(independent = diag(2), correlated = diag(2) + 2)) %>% generate(1) meta_list_out # generates S_index column # define can also take arguments as a list using the .list argument meta_list_out_2 = specify(a = ~ MASS::mvrnorm(n, rep(0, 2), Sigma = S)) %>% define(.list = list(n = c(10, 20, 30), S = list(independent = diag(2), correlated = diag(2) + 2))) %>% generate(1)
Takes simulated data from
generate
and applies functions to it, usually
model-fitting functions.
## S3 method for class 'simpr_tibble' fit( object, ..., .quiet = TRUE, .warn_on_error = TRUE, .stop_on_error = FALSE, .debug = FALSE, .progress = FALSE, .options = furrr_options() ) ## S3 method for class 'simpr_spec' fit( object, ..., .quiet = TRUE, .warn_on_error = TRUE, .stop_on_error = FALSE, .debug = FALSE, .progress = FALSE, .options = furrr_options() )## S3 method for class 'simpr_tibble' fit( object, ..., .quiet = TRUE, .warn_on_error = TRUE, .stop_on_error = FALSE, .debug = FALSE, .progress = FALSE, .options = furrr_options() ) ## S3 method for class 'simpr_spec' fit( object, ..., .quiet = TRUE, .warn_on_error = TRUE, .stop_on_error = FALSE, .debug = FALSE, .progress = FALSE, .options = furrr_options() )
object |
a |
... |
|
.quiet |
Should simulation errors be broadcast to the user as they occur? |
.warn_on_error |
Should there be a warning
when simulation errors occur? See
|
.stop_on_error |
Should the simulation stop immediately when simulation errors occur? |
.debug |
Run simulation in debug mode, allowing objects, etc. to be explored for each attempt to fit objects. |
.progress |
A logical, for whether or not to print a progress bar for multiprocess, multisession, and multicore plans . |
.options |
The |
This is the fourth step in the simulation
process: after generating the simulation data,
apply functions such as fitting a statistical
model to the data. The output is often then
passed to tidy_fits or
glance_fits to extract relevant
model estimates from the object.
Similar to
specify, the
model-fitting ... arguments can be
arbitrary R expressions (purrr-style
lambda functions, see
as_mapper) to specify
fitting models to the data. The functions are
computed within each simulation cell, so
dataset names are generally unnecessary: e.g.,
to compute regressions on each cell,
fit(linear_model = ~ lm(c ~ a + b). If
your modeling function requires a reference to
the full dataset, use ., e.g.
fit(linear_model = ~lm(c ~ a + b, data =
.).
a simpr_tibble object with
additional list-columns for the output of the
provided functions (e.g. model outputs). Just
like the output of
generate,
there is one row per repetition per
combination of metaparameters, and the
columns are the repetition number rep,
the metaparameter names, the simulated data
sim, with additional columns for the
function outputs specified in ....
If per_sim was called
previously, fit returns the object to
default simpr_tibble mode.
## Generate data to fit models simple_linear_data = specify(a = ~ 2 + rnorm(n), b = ~ 5 + 3*a + rnorm(n, 0, sd = 0.5)) %>% define(n = 100:101) %>% generate(2) ## Fit with a single linear term linear_fit = simple_linear_data %>% fit(linear = ~lm(b ~ a, data = .)) linear_fit # first fit element also prints ## Each element of $linear is a model object linear_fit$linear ## We can fit multiple models to the same data multi_fit = simple_linear_data %>% fit(linear = ~lm(b ~ a, data = .), quadratic = ~lm(b ~ a + I(a^2), data = .)) ## Two columns, one for each model multi_fit ## Again, each element is a model object multi_fit$quadratic ## Can view terms more nicely with tidy_fits multi_fit %>% tidy_fits ## Can view model summaries with glance_fits multi_fit %>% glance_fits ## Fit functions do not actually need to be any particular kind of model, they ## can be any arbitrary function. However, not all functions will lead to useful ## output with tidy_fits and glance_fits. add_five_data = simple_linear_data %>% fit(add_five = ~ . + 5) ## adds 5 to every value in dataset add_five_data## Generate data to fit models simple_linear_data = specify(a = ~ 2 + rnorm(n), b = ~ 5 + 3*a + rnorm(n, 0, sd = 0.5)) %>% define(n = 100:101) %>% generate(2) ## Fit with a single linear term linear_fit = simple_linear_data %>% fit(linear = ~lm(b ~ a, data = .)) linear_fit # first fit element also prints ## Each element of $linear is a model object linear_fit$linear ## We can fit multiple models to the same data multi_fit = simple_linear_data %>% fit(linear = ~lm(b ~ a, data = .), quadratic = ~lm(b ~ a + I(a^2), data = .)) ## Two columns, one for each model multi_fit ## Again, each element is a model object multi_fit$quadratic ## Can view terms more nicely with tidy_fits multi_fit %>% tidy_fits ## Can view model summaries with glance_fits multi_fit %>% glance_fits ## Fit functions do not actually need to be any particular kind of model, they ## can be any arbitrary function. However, not all functions will lead to useful ## output with tidy_fits and glance_fits. add_five_data = simple_linear_data %>% fit(add_five = ~ . + 5) ## adds 5 to every value in dataset add_five_data
Use specification from
specify or
define to produce simulated data.
## S3 method for class 'simpr_spec' generate( x, .reps, ..., .sim_name = "sim", .quiet = TRUE, .warn_on_error = TRUE, .stop_on_error = FALSE, .debug = FALSE, .progress = FALSE, .options = furrr_options(seed = TRUE) )## S3 method for class 'simpr_spec' generate( x, .reps, ..., .sim_name = "sim", .quiet = TRUE, .warn_on_error = TRUE, .stop_on_error = FALSE, .debug = FALSE, .progress = FALSE, .options = furrr_options(seed = TRUE) )
x |
a |
.reps |
number of replications to run (a whole number greater than 0) |
... |
filtering criteria for which rows
to simulate, passed to
|
.sim_name |
name of the list-column to be
created, containing simulation results.
Default is |
.quiet |
Should simulation errors be broadcast to the user as they occur? |
.warn_on_error |
Should there be a warning
when simulation errors occur? See
|
.stop_on_error |
Should the simulation stop immediately when simulation errors occur? |
.debug |
Run simulation in debug mode, allowing objects, etc. to be explored for each generated variable specification. |
.progress |
A logical, for whether or not to print a progress bar for multiprocess, multisession, and multicore plans. |
.options |
The |
This is the third step in the simulation
process: after specifying the population model
and defining the metaparameters, if any,
generate is the workhorse function that
actually generates the simulated datasets, one
for each replication and combination of
metaparameters. You likely want to use the
output of generate to fit model(s) with
fit.
Errors you get using this function usually have
to do with how you specified the simulation in
specify and
define.
a simpr_sims object,
which is a tibble with a row for each
repetition (a total of rep
repetitions) for each combination of
metaparameters and some extra metadata used
by fit. The
columns are rep for the repetition
number, the names of the metaparameters, and
a list-column (named by the argument
sim_name) containing the dataset for
each repetition and metaparameter
combination. simpr_sims objects can be
manipulated elementwise by dplyr and
tidyr verbs: the command is applied to
each element of the simulation list-column.
specify and
define for examples of how
these functions affect the output of
generate. See
vignette("Optimization") and the
furrr website for more information on
working with futures:
https://furrr.futureverse.org/
meta_list_out = specify(a = ~ MASS::mvrnorm(n, rep(0, 2), Sigma = S)) %>% define(n = c(10, 20, 30), S = list(independent = diag(2), correlated = diag(2) + 2)) %>% generate(1) ## View overall structure of the result and a single simulation output meta_list_out ## Changing .reps will change the number of replications and thus the number of ## rows in the output meta_list_2 = specify(a = ~ MASS::mvrnorm(n, rep(0, 2), Sigma = S)) %>% define(n = c(10, 20, 30), S = list(independent = diag(2), correlated = diag(2) + 2)) %>% generate(2) meta_list_2 ## Fitting, tidying functions can be included in this step by running those functions and then ## generate. This can save computation time when doing large ## simulations, especially with parallel processing meta_list_generate_after = specify(a = ~ MASS::mvrnorm(n, rep(0, 2), Sigma = S)) %>% define(n = c(10, 20, 30), S = list(independent = diag(2), correlated = diag(2) + 2)) %>% fit(lm = ~ lm(a_2 ~ a_1, data = .)) %>% tidy_fits %>% generate(1) meta_list_generate_aftermeta_list_out = specify(a = ~ MASS::mvrnorm(n, rep(0, 2), Sigma = S)) %>% define(n = c(10, 20, 30), S = list(independent = diag(2), correlated = diag(2) + 2)) %>% generate(1) ## View overall structure of the result and a single simulation output meta_list_out ## Changing .reps will change the number of replications and thus the number of ## rows in the output meta_list_2 = specify(a = ~ MASS::mvrnorm(n, rep(0, 2), Sigma = S)) %>% define(n = c(10, 20, 30), S = list(independent = diag(2), correlated = diag(2) + 2)) %>% generate(2) meta_list_2 ## Fitting, tidying functions can be included in this step by running those functions and then ## generate. This can save computation time when doing large ## simulations, especially with parallel processing meta_list_generate_after = specify(a = ~ MASS::mvrnorm(n, rep(0, 2), Sigma = S)) %>% define(n = c(10, 20, 30), S = list(independent = diag(2), correlated = diag(2) + 2)) %>% fit(lm = ~ lm(a_2 ~ a_1, data = .)) %>% tidy_fits %>% generate(1) meta_list_generate_after
Turn fitted models of simulated data (from
fit) into a
tidy tibble of model summaries, each with one line (via
broom::glance).
glance_fits(obj, ..., .progress = FALSE, .options = furrr_options())glance_fits(obj, ..., .progress = FALSE, .options = furrr_options())
obj |
tibble with repetition number,
metaparameters, simulated data, and fitted
models, from
|
... |
Additional arguments to
|
.progress |
A logical, for whether or not to print a progress bar for multiprocess, multisession, and multicore plans . |
.options |
The |
This the fifth step of the
simulation process: after fitting the model
with fit, now
tidy the model output for further analysis such
as evaluating power. All model objects should
be supported by
broom::glance.
The output of this function is quite useful comparing
overall model fits; see
Examples. For looking at specific
features of the model such as tests for
individual parameter estimates, use
tidy_fits.
a tibble with the output of the
broom::glance
method for the given object.
tidy_fits to view model
components (e.g. parameter estimates),
apply_fits to apply an
arbitrary function to the fits
simple_linear_data = specify(a = ~ 2 + rnorm(n), b = ~ 5 + 3 * x1 + rnorm(n, 0, sd = 0.5)) %>% define(n = 100:101) %>% generate(2) ## Can show tidy output for multiple competing models, compare_degree = simple_linear_data %>% fit(linear = ~lm(a ~ b, data = .), quadratic = ~lm(a ~ b + I(b^2), data = .)) %>% glance_fits compare_degree ## Models can be anything supported by broom::tidy. cor_vs_lm = simple_linear_data %>% fit(linear = ~lm(a ~ b, data = .), cor = ~ cor.test(.$a, .$b)) %>% glance_fits cor_vs_lm # has NA for non-matching termssimple_linear_data = specify(a = ~ 2 + rnorm(n), b = ~ 5 + 3 * x1 + rnorm(n, 0, sd = 0.5)) %>% define(n = 100:101) %>% generate(2) ## Can show tidy output for multiple competing models, compare_degree = simple_linear_data %>% fit(linear = ~lm(a ~ b, data = .), quadratic = ~lm(a ~ b + I(b^2), data = .)) %>% glance_fits compare_degree ## Models can be anything supported by broom::tidy. cor_vs_lm = simple_linear_data %>% fit(linear = ~lm(a ~ b, data = .), cor = ~ cor.test(.$a, .$b)) %>% glance_fits cor_vs_lm # has NA for non-matching terms
Allows applying data transformations to every simulation result with syntax as if dealing with a single simulation result using dplyr and tidyr verbs
per_sim(obj)per_sim(obj)
obj |
A |
After producing simulation results (a
simpr_tibble object), it is sometimes
needed to do some data transformation to
prepare for analysis. This can always be
specified in specify through custom
functions, but per_sim allows you to
also easily specify this in your pipeline.
After running per_sim, you can use the dplyr
and tidyr verbs you would use on a single
simulation result and it will be applied to all
results.
If, after running per_sim, you wish to
return to the default behavior to access
simpr_tibble results as a tibble with a
list_column for simulation results again, run
whole_tibble.
A simpr_sims object for use with dplyr and tidyr verbs.
## Often most convenient to specify simulations for 'wide' data data_wide = specify(a = ~ runif(5, min = 0, max = 1), b = ~ runif(5, min = 0, max = 2)) %>% generate(2) data_wide ## Any dplyr or tidyr verbs can be applied after per_sim() data_long = data_wide %>% per_sim() %>% pivot_longer(everything(), names_to = "name", values_to = "value") data_long ## Now, ready for analysis data_long %>% fit(lm = ~lm(value ~ name)) %>% tidy_fits## Often most convenient to specify simulations for 'wide' data data_wide = specify(a = ~ runif(5, min = 0, max = 1), b = ~ runif(5, min = 0, max = 2)) %>% generate(2) data_wide ## Any dplyr or tidyr verbs can be applied after per_sim() data_long = data_wide %>% per_sim() %>% pivot_longer(everything(), names_to = "name", values_to = "value") data_long ## Now, ready for analysis data_long %>% fit(lm = ~lm(value ~ name)) %>% tidy_fits
Accessor & display methods for simpr_spec class
## S3 method for class 'simpr_spec' print(x, ...) new_simpr_spec() is.simpr_spec(x)## S3 method for class 'simpr_spec' print(x, ...) new_simpr_spec() is.simpr_spec(x)
x |
a |
... |
ignored |
Class simpr_spec is created by
specify and/or
define to specify the simulation
variables, which is produced by
generate.
The print method provides an overview of the
specification, including the conditions.
print.simpr_spec has no return value
and is called for its side-effects.
new_simpr_spec returns an empty
simpr_spec object. is.simpr_spec returns
a length-1 logical vector, TRUE or FALSE,
which indicates whether an object is a
simpr_spec.
empty = new_simpr_spec() print(empty) ## Easiest to create a simpr_spec with specify simple_spec = specify(a = ~ rbinom(n, size, prob)) print(simple_spec) ## Adding on define adds all possible combinations ## of conditions and more info in output. defined_spec = specify(a = ~ rbinom(n, size, prob)) %>% define(n = c(10, 20), size = c(20, 40), prob = c(0.2, 0.4)) print(defined_spec)empty = new_simpr_spec() print(empty) ## Easiest to create a simpr_spec with specify simple_spec = specify(a = ~ rbinom(n, size, prob)) print(simple_spec) ## Adding on define adds all possible combinations ## of conditions and more info in output. defined_spec = specify(a = ~ rbinom(n, size, prob)) %>% define(n = c(10, 20), size = c(20, 40), prob = c(0.2, 0.4)) print(defined_spec)
Specify the data-generating mechanisms for the simulation using purrr-style lambda functions.
## S3 method for class 'formula' specify(x = NULL, ..., .use_names = TRUE, .sep = "_")## S3 method for class 'formula' specify(x = NULL, ..., .use_names = TRUE, .sep = "_")
x |
leave this argument blank (NULL); this argument is a placeholder and can be skipped. |
... |
named |
.use_names |
Whether to use names generated by the lambda function (TRUE, the default), or to overwrite them with supplied names. |
.sep |
Specify the separator for auto-generating names. See Column naming. |
This is always the first command in the
simulation process, to specify the actual
simulated variables, which is then passed to
define to define metaparameters
and then to
generate to
generate the data.
The ... arguments use an efficient
syntax to specify custom functions needed for
generating a simulation, based on the
purrr package. When producing one
variable, one can provide an expression such as
specify(a = ~ 3 + runif(10)); the
expression is preceded by ~, the tilde
operator, and can refer to previous arguments
in specify or to metaparameters in
define. This is called a lambda
function.
Order matters: arguments are evaluated
sequentially, so later argument can refer to an
earlier one, e.g. specify(a = ~ rnorm(2),
b = ~ a + rnorm(2)).
generate
combines results together into a single tibble
for each simulation, so all lambda functions
should produce the same number of rows.
However, a lambda function can produce multiple
columns.
A simpr_specify object which
contains the functions needed to generate the
simulation; to be passed to
define for defining
metaparameters or, if there are no
metaparameters, directly to
generate
for generating the simulation.
Also useful is the fact that one can refer to
variables in subsequent arguments. So, one
could define another variable b that
depends on a very simply, e.g.
specify(a = ~ 3 + runif(10), b = ~ 2 *
x).
Finally, one can also refer to metaparameters
that are to be systematically varied in the
simulation study. See define
and the examples for more details.
Because functions can produce different
numbers of columns, there are several options
for naming columns. If a provided lambda
function produces a single column, the name
given to the argument becomes the name of the
column. If the lambda function already
produces column names, then the output will
use these names if .use_names = TRUE,
the default. Otherwise, simpr uses the
argument name as a base and auto-numbers the
columns. For instance, if the argument
a generates a two-column matrix and
.sep = "_" (the default) the columns
will be named a_1and a_2.
Custom names can also be directly provided by
a double-sided formula. The left-hand side
must use c or
cbind, e.g. specify(c(a,
b) ~ MASS::mvrnorm(5, c(0, 0), Sigma =
diag(2))).
This function is an S3 method for
specify from the
generics package. Because x is
a formal argument of
specify, if you have
a variable in your simulation named x
it will be automatically moved to be the
first variable (with a message). It is therefore
safest to use any other variable name besides
x.
## specify a variable and generate it in the simulation single_var = specify(a = ~ 1 + rnorm(5)) %>% generate(1) # generate a single repetition of the simulation single_var two_var = specify(a = ~ 1 + rnorm(5), b = ~ x + 2) %>% generate(1) two_var ## Generates a_01 through a_10 autonumber_var = specify(a = ~ MASS::mvrnorm(5, rep(0, 10), Sigma = diag(10))) %>% generate(1) autonumber_var # alternatively, you could use a two-sided formula for names multi_name = specify(cbind(a, b, c) ~ MASS::mvrnorm(5, rep(0, 3), Sigma = diag(3))) %>% generate(1) multi_name # Simple example of setting a metaparameter simple_meta = specify(a = ~ 1 + rnorm(n)) %>% define(n = c(5, 10)) %>% # without this line you would get an error! generate(1) simple_meta # has two rows now, one for each value of n simple_meta$sim[[1]] # n = 5 simple_meta$sim[[2]] # n = 10## specify a variable and generate it in the simulation single_var = specify(a = ~ 1 + rnorm(5)) %>% generate(1) # generate a single repetition of the simulation single_var two_var = specify(a = ~ 1 + rnorm(5), b = ~ x + 2) %>% generate(1) two_var ## Generates a_01 through a_10 autonumber_var = specify(a = ~ MASS::mvrnorm(5, rep(0, 10), Sigma = diag(10))) %>% generate(1) autonumber_var # alternatively, you could use a two-sided formula for names multi_name = specify(cbind(a, b, c) ~ MASS::mvrnorm(5, rep(0, 3), Sigma = diag(3))) %>% generate(1) multi_name # Simple example of setting a metaparameter simple_meta = specify(a = ~ 1 + rnorm(n)) %>% define(n = c(5, 10)) %>% # without this line you would get an error! generate(1) simple_meta # has two rows now, one for each value of n simple_meta$sim[[1]] # n = 5 simple_meta$sim[[2]] # n = 10
Turn models fit to simulated data (from
fit) into a
tidy tibble of model estimates (via
broom::tidy).
tidy_fits(obj, ..., .progress = FALSE, .options = furrr_options())tidy_fits(obj, ..., .progress = FALSE, .options = furrr_options())
obj |
a |
... |
Additional arguments to the
|
.progress |
A logical, for whether or not to print a progress bar for multiprocess, multisession, and multicore plans . |
.options |
The |
This the fifth step of the simulation process:
after fitting the model with
fit, now tidy
the model output for further analysis such as
evaluating power. All model objects should be
supported by
broom::tidy. See
apply_fits for applying any
arbitrary function to the data, including other
tidiers.
The output of this function is quite useful for
diagnosing bias, precision, and power. For
looking at overall features of the model (e.g.,
R-squared), use glance_fits.
a tibble with the output of the
broom::tidy method
applied to each model fit and then bound into
a single tibble.
glance_fits to view
overall model statistics (e.g. R-squared),
apply_fits to apply an
arbitrary function to the fits
simple_linear_data = specify(a = ~ 2 + rnorm(n), b = ~ 5 + 3 * x1 + rnorm(n, 0, sd = 0.5)) %>% define(n = 100:101) %>% generate(2) ## Can show tidy output for multiple competing models, compare_degree = simple_linear_data %>% fit(linear = ~lm(a ~ b, data = .), quadratic = ~lm(a ~ b + I(b^2), data = .)) %>% tidy_fits compare_degree ## Models can be anything supported by broom::tidy. cor_vs_lm = simple_linear_data %>% fit(linear = ~lm(a ~ b, data = .), cor = ~ cor.test(.$a, .$b)) %>% tidy_fits cor_vs_lm # has NA for non-matching termssimple_linear_data = specify(a = ~ 2 + rnorm(n), b = ~ 5 + 3 * x1 + rnorm(n, 0, sd = 0.5)) %>% define(n = 100:101) %>% generate(2) ## Can show tidy output for multiple competing models, compare_degree = simple_linear_data %>% fit(linear = ~lm(a ~ b, data = .), quadratic = ~lm(a ~ b + I(b^2), data = .)) %>% tidy_fits compare_degree ## Models can be anything supported by broom::tidy. cor_vs_lm = simple_linear_data %>% fit(linear = ~lm(a ~ b, data = .), cor = ~ cor.test(.$a, .$b)) %>% tidy_fits cor_vs_lm # has NA for non-matching terms
These are simpr-compatible methods for generic
dplyr and tidyr verbs. The user
is not expected to call these methods directly.
## S3 method for class 'simpr_sims' add_count( x, ..., wt = NULL, sort = FALSE, name = NULL, .drop = lifecycle::deprecated() ) ## S3 method for class 'simpr_spec' add_count( x, ..., wt = NULL, sort = FALSE, name = NULL, .drop = lifecycle::deprecated() ) ## S3 method for class 'simpr_sims' anti_join(x, y, by = NULL, copy = FALSE, ...) ## S3 method for class 'simpr_spec' anti_join(x, y, by = NULL, copy = FALSE, ...) ## S3 method for class 'simpr_sims' arrange_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' arrange_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' arrange(.data, ..., .by_group = FALSE) ## S3 method for class 'simpr_spec' arrange(.data, ..., .by_group = FALSE) ## S3 method for class 'simpr_sims' as.tbl(x, ...) ## S3 method for class 'simpr_spec' as.tbl(x, ...) ## S3 method for class 'simpr_sims' auto_copy(x, y, copy = FALSE, ...) ## S3 method for class 'simpr_spec' auto_copy(x, y, copy = FALSE, ...) ## S3 method for class 'simpr_sims' collect(x, ...) ## S3 method for class 'simpr_spec' collect(x, ...) ## S3 method for class 'simpr_sims' compute(x, ...) ## S3 method for class 'simpr_spec' compute(x, ...) ## S3 method for class 'simpr_sims' count(x, ..., wt = NULL, sort = FALSE, name = NULL) ## S3 method for class 'simpr_spec' count(x, ..., wt = NULL, sort = FALSE, name = NULL) ## S3 method for class 'simpr_sims' distinct_(.data, ..., .dots, .keep_all = FALSE) ## S3 method for class 'simpr_spec' distinct_(.data, ..., .dots, .keep_all = FALSE) ## S3 method for class 'simpr_sims' distinct(.data, ..., .keep_all = FALSE) ## S3 method for class 'simpr_spec' distinct(.data, ..., .keep_all = FALSE) ## S3 method for class 'simpr_sims' do_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' do_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' do(.data, ...) ## S3 method for class 'simpr_spec' do(.data, ...) ## S3 method for class 'simpr_sims' dplyr_col_modify(data, cols) ## S3 method for class 'simpr_spec' dplyr_col_modify(data, cols) ## S3 method for class 'simpr_sims' dplyr_reconstruct(data, template) ## S3 method for class 'simpr_spec' dplyr_reconstruct(data, template) ## S3 method for class 'simpr_sims' dplyr_row_slice(data, i, ...) ## S3 method for class 'simpr_spec' dplyr_row_slice(data, i, ...) ## S3 method for class 'simpr_sims' filter_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' filter_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' filter(.data, ..., .preserve = FALSE) ## S3 method for class 'simpr_spec' filter(.data, ..., .preserve = FALSE) ## S3 method for class 'simpr_sims' full_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_spec' full_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_sims' group_by_(.data, ..., .dots = list(), add = FALSE) ## S3 method for class 'simpr_spec' group_by_(.data, ..., .dots = list(), add = FALSE) ## S3 method for class 'simpr_sims' group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data)) ## S3 method for class 'simpr_spec' group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data)) ## S3 method for class 'simpr_sims' group_data(.data) ## S3 method for class 'simpr_spec' group_data(.data) ## S3 method for class 'simpr_sims' group_indices_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' group_indices_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' group_indices(.data, ...) ## S3 method for class 'simpr_spec' group_indices(.data, ...) ## S3 method for class 'simpr_sims' group_keys(.tbl, ...) ## S3 method for class 'simpr_spec' group_keys(.tbl, ...) ## S3 method for class 'simpr_sims' group_map(.data, .f, ..., .keep = FALSE) ## S3 method for class 'simpr_spec' group_map(.data, .f, ..., .keep = FALSE) ## S3 method for class 'simpr_sims' group_modify(.data, .f, ..., .keep = FALSE) ## S3 method for class 'simpr_spec' group_modify(.data, .f, ..., .keep = FALSE) ## S3 method for class 'simpr_sims' group_nest(.tbl, ..., .key = "data", keep = FALSE) ## S3 method for class 'simpr_spec' group_nest(.tbl, ..., .key = "data", keep = FALSE) ## S3 method for class 'simpr_sims' group_size(x) ## S3 method for class 'simpr_spec' group_size(x) ## S3 method for class 'simpr_sims' group_split(.tbl, ..., .keep = TRUE) ## S3 method for class 'simpr_spec' group_split(.tbl, ..., .keep = TRUE) ## S3 method for class 'simpr_sims' group_trim(.tbl, .drop = dplyr::group_by_drop_default(.tbl)) ## S3 method for class 'simpr_spec' group_trim(.tbl, .drop = dplyr::group_by_drop_default(.tbl)) ## S3 method for class 'simpr_sims' group_vars(x) ## S3 method for class 'simpr_spec' group_vars(x) ## S3 method for class 'simpr_sims' groups(x) ## S3 method for class 'simpr_spec' groups(x) ## S3 method for class 'simpr_sims' inner_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_spec' inner_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_sims' intersect(x, y, ...) ## S3 method for class 'simpr_spec' intersect(x, y, ...) ## S3 method for class 'simpr_sims' left_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_spec' left_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_sims' mutate_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' mutate_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' mutate(.data, ...) ## S3 method for class 'simpr_spec' mutate(.data, ...) ## S3 method for class 'simpr_sims' n_groups(x) ## S3 method for class 'simpr_spec' n_groups(x) ## S3 method for class 'simpr_sims' nest_by(.data, ..., .key = "data", .keep = FALSE) ## S3 method for class 'simpr_spec' nest_by(.data, ..., .key = "data", .keep = FALSE) ## S3 method for class 'simpr_sims' nest_join(x, y, by = NULL, copy = FALSE, keep = FALSE, name = NULL, ...) ## S3 method for class 'simpr_spec' nest_join(x, y, by = NULL, copy = FALSE, keep = FALSE, name = NULL, ...) ## S3 method for class 'simpr_sims' pull(.data, var = -1, name = NULL, ...) ## S3 method for class 'simpr_spec' pull(.data, var = -1, name = NULL, ...) ## S3 method for class 'simpr_sims' relocate(.data, ..., .before = NULL, .after = NULL) ## S3 method for class 'simpr_spec' relocate(.data, ..., .before = NULL, .after = NULL) ## S3 method for class 'simpr_sims' rename_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' rename_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' rename_with(.data, .fn, .cols = dplyr::everything(), ...) ## S3 method for class 'simpr_spec' rename_with(.data, .fn, .cols = dplyr::everything(), ...) ## S3 method for class 'simpr_sims' rename(.data, ...) ## S3 method for class 'simpr_spec' rename(.data, ...) ## S3 method for class 'simpr_sims' right_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_spec' right_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_sims' rows_delete(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_spec' rows_delete(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_sims' rows_insert(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_spec' rows_insert(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_sims' rows_patch(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_spec' rows_patch(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_sims' rows_update(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_spec' rows_update(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_sims' rows_upsert(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_spec' rows_upsert(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_sims' rowwise(data, ...) ## S3 method for class 'simpr_spec' rowwise(data, ...) ## S3 method for class 'simpr_sims' same_src(x, y) ## S3 method for class 'simpr_spec' same_src(x, y) ## S3 method for class 'simpr_sims' sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = NULL, ...) ## S3 method for class 'simpr_spec' sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = NULL, ...) ## S3 method for class 'simpr_sims' sample_n(tbl, size, replace = FALSE, weight = NULL, .env = NULL, ...) ## S3 method for class 'simpr_spec' sample_n(tbl, size, replace = FALSE, weight = NULL, .env = NULL, ...) ## S3 method for class 'simpr_sims' select_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' select_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' select(.data, ...) ## S3 method for class 'simpr_spec' select(.data, ...) ## S3 method for class 'simpr_sims' semi_join(x, y, by = NULL, copy = FALSE, ...) ## S3 method for class 'simpr_spec' semi_join(x, y, by = NULL, copy = FALSE, ...) ## S3 method for class 'simpr_sims' setdiff(x, y, ...) ## S3 method for class 'simpr_spec' setdiff(x, y, ...) ## S3 method for class 'simpr_sims' setequal(x, y, ...) ## S3 method for class 'simpr_spec' setequal(x, y, ...) ## S3 method for class 'simpr_sims' slice_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' slice_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' slice_head(.data, ..., n, prop) ## S3 method for class 'simpr_spec' slice_head(.data, ..., n, prop) ## S3 method for class 'simpr_sims' slice_max(.data, order_by, ..., n, prop, with_ties = TRUE) ## S3 method for class 'simpr_spec' slice_max(.data, order_by, ..., n, prop, with_ties = TRUE) ## S3 method for class 'simpr_sims' slice_min(.data, order_by, ..., n, prop, with_ties = TRUE) ## S3 method for class 'simpr_spec' slice_min(.data, order_by, ..., n, prop, with_ties = TRUE) ## S3 method for class 'simpr_sims' slice_sample(.data, ..., n, prop, weight_by = NULL, replace = FALSE) ## S3 method for class 'simpr_spec' slice_sample(.data, ..., n, prop, weight_by = NULL, replace = FALSE) ## S3 method for class 'simpr_sims' slice_tail(.data, ..., n, prop) ## S3 method for class 'simpr_spec' slice_tail(.data, ..., n, prop) ## S3 method for class 'simpr_sims' slice(.data, ..., .preserve = FALSE) ## S3 method for class 'simpr_spec' slice(.data, ..., .preserve = FALSE) ## S3 method for class 'simpr_sims' summarise_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' summarise_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' summarise(.data, ..., .groups = NULL) ## S3 method for class 'simpr_spec' summarise(.data, ..., .groups = NULL) ## S3 method for class 'simpr_sims' tally(x, wt = NULL, sort = FALSE, name = NULL) ## S3 method for class 'simpr_spec' tally(x, wt = NULL, sort = FALSE, name = NULL) ## S3 method for class 'simpr_sims' tbl_vars(x) ## S3 method for class 'simpr_spec' tbl_vars(x) ## S3 method for class 'simpr_sims' transmute_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' transmute_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' transmute(.data, ...) ## S3 method for class 'simpr_spec' transmute(.data, ...) ## S3 method for class 'simpr_sims' ungroup(x, ...) ## S3 method for class 'simpr_spec' ungroup(x, ...) ## S3 method for class 'simpr_sims' union_all(x, y, ...) ## S3 method for class 'simpr_spec' union_all(x, y, ...) ## S3 method for class 'simpr_sims' union(x, y, ...) ## S3 method for class 'simpr_spec' union(x, y, ...) ## S3 method for class 'simpr_sims' complete_(data, cols, fill = list(), ...) ## S3 method for class 'simpr_spec' complete_(data, cols, fill = list(), ...) ## S3 method for class 'simpr_sims' complete(data, ..., fill = list()) ## S3 method for class 'simpr_spec' complete(data, ..., fill = list()) ## S3 method for class 'simpr_sims' drop_na_(data, vars) ## S3 method for class 'simpr_spec' drop_na_(data, vars) ## S3 method for class 'simpr_sims' drop_na(data, ...) ## S3 method for class 'simpr_spec' drop_na(data, ...) ## S3 method for class 'simpr_sims' expand_(data, dots, ...) ## S3 method for class 'simpr_spec' expand_(data, dots, ...) ## S3 method for class 'simpr_sims' expand(data, ..., .name_repair = "check_unique") ## S3 method for class 'simpr_spec' expand(data, ..., .name_repair = "check_unique") ## S3 method for class 'simpr_sims' extract_( data, col, into, regex = "([[:alnum:]]+)", remove = TRUE, convert = FALSE, ... ) ## S3 method for class 'simpr_spec' extract_( data, col, into, regex = "([[:alnum:]]+)", remove = TRUE, convert = FALSE, ... ) ## S3 method for class 'simpr_sims' extract( data, col, into, regex = "([[:alnum:]]+)", remove = TRUE, convert = FALSE, ... ) ## S3 method for class 'simpr_spec' extract( data, col, into, regex = "([[:alnum:]]+)", remove = TRUE, convert = FALSE, ... ) ## S3 method for class 'simpr_sims' fill_(data, fill_cols, .direction = c("down", "up")) ## S3 method for class 'simpr_spec' fill_(data, fill_cols, .direction = c("down", "up")) ## S3 method for class 'simpr_sims' fill(data, ..., .direction = c("down", "up", "downup", "updown")) ## S3 method for class 'simpr_spec' fill(data, ..., .direction = c("down", "up", "downup", "updown")) ## S3 method for class 'simpr_sims' gather_( data, key_col, value_col, gather_cols, na.rm = FALSE, convert = FALSE, factor_key = FALSE ) ## S3 method for class 'simpr_spec' gather_( data, key_col, value_col, gather_cols, na.rm = FALSE, convert = FALSE, factor_key = FALSE ) ## S3 method for class 'simpr_sims' gather( data, key = "key", value = "value", ..., na.rm = FALSE, convert = FALSE, factor_key = FALSE ) ## S3 method for class 'simpr_spec' gather( data, key = "key", value = "value", ..., na.rm = FALSE, convert = FALSE, factor_key = FALSE ) ## S3 method for class 'simpr_sims' nest_legacy(data, ..., .key = "data") ## S3 method for class 'simpr_spec' nest_legacy(data, ..., .key = "data") ## S3 method for class 'simpr_sims' nest(.data, ..., .names_sep = NULL, .key = lifecycle::deprecated()) ## S3 method for class 'simpr_spec' nest(.data, ..., .names_sep = NULL, .key = lifecycle::deprecated()) ## S3 method for class 'simpr_sims' pivot_longer( data, cols, names_to = "name", names_prefix = NULL, names_sep = NULL, names_pattern = NULL, names_ptypes = list(), names_transform = list(), names_repair = "check_unique", values_to = "value", values_drop_na = FALSE, values_ptypes = list(), values_transform = list(), ... ) ## S3 method for class 'simpr_spec' pivot_longer( data, cols, names_to = "name", names_prefix = NULL, names_sep = NULL, names_pattern = NULL, names_ptypes = list(), names_transform = list(), names_repair = "check_unique", values_to = "value", values_drop_na = FALSE, values_ptypes = list(), values_transform = list(), ... ) ## S3 method for class 'simpr_sims' pivot_wider( data, id_cols = NULL, id_expand = FALSE, names_from = NULL, names_prefix = "", names_sep = "_", names_glue = NULL, names_sort = FALSE, names_vary = "fastest", names_expand = FALSE, names_repair = "check_unique", values_from = NULL, values_fill = NULL, values_fn = NULL, unused_fn = NULL, ... ) ## S3 method for class 'simpr_spec' pivot_wider( data, id_cols = NULL, id_expand = FALSE, names_from = NULL, names_prefix = "", names_sep = "_", names_glue = NULL, names_sort = FALSE, names_vary = "fastest", names_expand = FALSE, names_repair = "check_unique", values_from = NULL, values_fill = NULL, values_fn = NULL, unused_fn = NULL, ... ) ## S3 method for class 'simpr_sims' replace_na(data, replace, ...) ## S3 method for class 'simpr_spec' replace_na(data, replace, ...) ## S3 method for class 'simpr_sims' separate_( data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ... ) ## S3 method for class 'simpr_spec' separate_( data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ... ) ## S3 method for class 'simpr_sims' separate_rows_(data, cols, sep = "[^[:alnum:].]+", convert = FALSE) ## S3 method for class 'simpr_spec' separate_rows_(data, cols, sep = "[^[:alnum:].]+", convert = FALSE) ## S3 method for class 'simpr_sims' separate_rows(data, ..., sep = "[^[:alnum:].]+", convert = FALSE) ## S3 method for class 'simpr_spec' separate_rows(data, ..., sep = "[^[:alnum:].]+", convert = FALSE) ## S3 method for class 'simpr_sims' separate( data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ... ) ## S3 method for class 'simpr_spec' separate( data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ... ) ## S3 method for class 'simpr_sims' spread_( data, key_col, value_col, fill = NA, convert = FALSE, drop = TRUE, sep = NULL ) ## S3 method for class 'simpr_spec' spread_( data, key_col, value_col, fill = NA, convert = FALSE, drop = TRUE, sep = NULL ) ## S3 method for class 'simpr_sims' spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE, sep = NULL) ## S3 method for class 'simpr_spec' spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE, sep = NULL) ## S3 method for class 'simpr_sims' unite_(data, col, from, sep = "_", remove = TRUE) ## S3 method for class 'simpr_spec' unite_(data, col, from, sep = "_", remove = TRUE) ## S3 method for class 'simpr_sims' unite(data, col, ..., sep = "_", remove = TRUE, na.rm = FALSE) ## S3 method for class 'simpr_spec' unite(data, col, ..., sep = "_", remove = TRUE, na.rm = FALSE) ## S3 method for class 'simpr_sims' unnest_legacy(data, ..., .drop = NA, .id = NULL, .sep = NULL, .preserve = NULL) ## S3 method for class 'simpr_spec' unnest_legacy(data, ..., .drop = NA, .id = NULL, .sep = NULL, .preserve = NULL) ## S3 method for class 'simpr_sims' unnest( data, cols, ..., keep_empty = FALSE, ptype = NULL, names_sep = NULL, names_repair = "check_unique", .drop = lifecycle::deprecated(), .id = lifecycle::deprecated(), .sep = lifecycle::deprecated(), .preserve = lifecycle::deprecated() ) ## S3 method for class 'simpr_spec' unnest( data, cols, ..., keep_empty = FALSE, ptype = NULL, names_sep = NULL, names_repair = "check_unique", .drop = lifecycle::deprecated(), .id = lifecycle::deprecated(), .sep = lifecycle::deprecated(), .preserve = lifecycle::deprecated() )## S3 method for class 'simpr_sims' add_count( x, ..., wt = NULL, sort = FALSE, name = NULL, .drop = lifecycle::deprecated() ) ## S3 method for class 'simpr_spec' add_count( x, ..., wt = NULL, sort = FALSE, name = NULL, .drop = lifecycle::deprecated() ) ## S3 method for class 'simpr_sims' anti_join(x, y, by = NULL, copy = FALSE, ...) ## S3 method for class 'simpr_spec' anti_join(x, y, by = NULL, copy = FALSE, ...) ## S3 method for class 'simpr_sims' arrange_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' arrange_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' arrange(.data, ..., .by_group = FALSE) ## S3 method for class 'simpr_spec' arrange(.data, ..., .by_group = FALSE) ## S3 method for class 'simpr_sims' as.tbl(x, ...) ## S3 method for class 'simpr_spec' as.tbl(x, ...) ## S3 method for class 'simpr_sims' auto_copy(x, y, copy = FALSE, ...) ## S3 method for class 'simpr_spec' auto_copy(x, y, copy = FALSE, ...) ## S3 method for class 'simpr_sims' collect(x, ...) ## S3 method for class 'simpr_spec' collect(x, ...) ## S3 method for class 'simpr_sims' compute(x, ...) ## S3 method for class 'simpr_spec' compute(x, ...) ## S3 method for class 'simpr_sims' count(x, ..., wt = NULL, sort = FALSE, name = NULL) ## S3 method for class 'simpr_spec' count(x, ..., wt = NULL, sort = FALSE, name = NULL) ## S3 method for class 'simpr_sims' distinct_(.data, ..., .dots, .keep_all = FALSE) ## S3 method for class 'simpr_spec' distinct_(.data, ..., .dots, .keep_all = FALSE) ## S3 method for class 'simpr_sims' distinct(.data, ..., .keep_all = FALSE) ## S3 method for class 'simpr_spec' distinct(.data, ..., .keep_all = FALSE) ## S3 method for class 'simpr_sims' do_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' do_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' do(.data, ...) ## S3 method for class 'simpr_spec' do(.data, ...) ## S3 method for class 'simpr_sims' dplyr_col_modify(data, cols) ## S3 method for class 'simpr_spec' dplyr_col_modify(data, cols) ## S3 method for class 'simpr_sims' dplyr_reconstruct(data, template) ## S3 method for class 'simpr_spec' dplyr_reconstruct(data, template) ## S3 method for class 'simpr_sims' dplyr_row_slice(data, i, ...) ## S3 method for class 'simpr_spec' dplyr_row_slice(data, i, ...) ## S3 method for class 'simpr_sims' filter_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' filter_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' filter(.data, ..., .preserve = FALSE) ## S3 method for class 'simpr_spec' filter(.data, ..., .preserve = FALSE) ## S3 method for class 'simpr_sims' full_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_spec' full_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_sims' group_by_(.data, ..., .dots = list(), add = FALSE) ## S3 method for class 'simpr_spec' group_by_(.data, ..., .dots = list(), add = FALSE) ## S3 method for class 'simpr_sims' group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data)) ## S3 method for class 'simpr_spec' group_by(.data, ..., .add = FALSE, .drop = dplyr::group_by_drop_default(.data)) ## S3 method for class 'simpr_sims' group_data(.data) ## S3 method for class 'simpr_spec' group_data(.data) ## S3 method for class 'simpr_sims' group_indices_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' group_indices_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' group_indices(.data, ...) ## S3 method for class 'simpr_spec' group_indices(.data, ...) ## S3 method for class 'simpr_sims' group_keys(.tbl, ...) ## S3 method for class 'simpr_spec' group_keys(.tbl, ...) ## S3 method for class 'simpr_sims' group_map(.data, .f, ..., .keep = FALSE) ## S3 method for class 'simpr_spec' group_map(.data, .f, ..., .keep = FALSE) ## S3 method for class 'simpr_sims' group_modify(.data, .f, ..., .keep = FALSE) ## S3 method for class 'simpr_spec' group_modify(.data, .f, ..., .keep = FALSE) ## S3 method for class 'simpr_sims' group_nest(.tbl, ..., .key = "data", keep = FALSE) ## S3 method for class 'simpr_spec' group_nest(.tbl, ..., .key = "data", keep = FALSE) ## S3 method for class 'simpr_sims' group_size(x) ## S3 method for class 'simpr_spec' group_size(x) ## S3 method for class 'simpr_sims' group_split(.tbl, ..., .keep = TRUE) ## S3 method for class 'simpr_spec' group_split(.tbl, ..., .keep = TRUE) ## S3 method for class 'simpr_sims' group_trim(.tbl, .drop = dplyr::group_by_drop_default(.tbl)) ## S3 method for class 'simpr_spec' group_trim(.tbl, .drop = dplyr::group_by_drop_default(.tbl)) ## S3 method for class 'simpr_sims' group_vars(x) ## S3 method for class 'simpr_spec' group_vars(x) ## S3 method for class 'simpr_sims' groups(x) ## S3 method for class 'simpr_spec' groups(x) ## S3 method for class 'simpr_sims' inner_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_spec' inner_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_sims' intersect(x, y, ...) ## S3 method for class 'simpr_spec' intersect(x, y, ...) ## S3 method for class 'simpr_sims' left_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_spec' left_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_sims' mutate_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' mutate_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' mutate(.data, ...) ## S3 method for class 'simpr_spec' mutate(.data, ...) ## S3 method for class 'simpr_sims' n_groups(x) ## S3 method for class 'simpr_spec' n_groups(x) ## S3 method for class 'simpr_sims' nest_by(.data, ..., .key = "data", .keep = FALSE) ## S3 method for class 'simpr_spec' nest_by(.data, ..., .key = "data", .keep = FALSE) ## S3 method for class 'simpr_sims' nest_join(x, y, by = NULL, copy = FALSE, keep = FALSE, name = NULL, ...) ## S3 method for class 'simpr_spec' nest_join(x, y, by = NULL, copy = FALSE, keep = FALSE, name = NULL, ...) ## S3 method for class 'simpr_sims' pull(.data, var = -1, name = NULL, ...) ## S3 method for class 'simpr_spec' pull(.data, var = -1, name = NULL, ...) ## S3 method for class 'simpr_sims' relocate(.data, ..., .before = NULL, .after = NULL) ## S3 method for class 'simpr_spec' relocate(.data, ..., .before = NULL, .after = NULL) ## S3 method for class 'simpr_sims' rename_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' rename_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' rename_with(.data, .fn, .cols = dplyr::everything(), ...) ## S3 method for class 'simpr_spec' rename_with(.data, .fn, .cols = dplyr::everything(), ...) ## S3 method for class 'simpr_sims' rename(.data, ...) ## S3 method for class 'simpr_spec' rename(.data, ...) ## S3 method for class 'simpr_sims' right_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_spec' right_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE ) ## S3 method for class 'simpr_sims' rows_delete(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_spec' rows_delete(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_sims' rows_insert(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_spec' rows_insert(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_sims' rows_patch(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_spec' rows_patch(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_sims' rows_update(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_spec' rows_update(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_sims' rows_upsert(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_spec' rows_upsert(x, y, by = NULL, ..., copy = FALSE, in_place = FALSE) ## S3 method for class 'simpr_sims' rowwise(data, ...) ## S3 method for class 'simpr_spec' rowwise(data, ...) ## S3 method for class 'simpr_sims' same_src(x, y) ## S3 method for class 'simpr_spec' same_src(x, y) ## S3 method for class 'simpr_sims' sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = NULL, ...) ## S3 method for class 'simpr_spec' sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = NULL, ...) ## S3 method for class 'simpr_sims' sample_n(tbl, size, replace = FALSE, weight = NULL, .env = NULL, ...) ## S3 method for class 'simpr_spec' sample_n(tbl, size, replace = FALSE, weight = NULL, .env = NULL, ...) ## S3 method for class 'simpr_sims' select_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' select_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' select(.data, ...) ## S3 method for class 'simpr_spec' select(.data, ...) ## S3 method for class 'simpr_sims' semi_join(x, y, by = NULL, copy = FALSE, ...) ## S3 method for class 'simpr_spec' semi_join(x, y, by = NULL, copy = FALSE, ...) ## S3 method for class 'simpr_sims' setdiff(x, y, ...) ## S3 method for class 'simpr_spec' setdiff(x, y, ...) ## S3 method for class 'simpr_sims' setequal(x, y, ...) ## S3 method for class 'simpr_spec' setequal(x, y, ...) ## S3 method for class 'simpr_sims' slice_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' slice_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' slice_head(.data, ..., n, prop) ## S3 method for class 'simpr_spec' slice_head(.data, ..., n, prop) ## S3 method for class 'simpr_sims' slice_max(.data, order_by, ..., n, prop, with_ties = TRUE) ## S3 method for class 'simpr_spec' slice_max(.data, order_by, ..., n, prop, with_ties = TRUE) ## S3 method for class 'simpr_sims' slice_min(.data, order_by, ..., n, prop, with_ties = TRUE) ## S3 method for class 'simpr_spec' slice_min(.data, order_by, ..., n, prop, with_ties = TRUE) ## S3 method for class 'simpr_sims' slice_sample(.data, ..., n, prop, weight_by = NULL, replace = FALSE) ## S3 method for class 'simpr_spec' slice_sample(.data, ..., n, prop, weight_by = NULL, replace = FALSE) ## S3 method for class 'simpr_sims' slice_tail(.data, ..., n, prop) ## S3 method for class 'simpr_spec' slice_tail(.data, ..., n, prop) ## S3 method for class 'simpr_sims' slice(.data, ..., .preserve = FALSE) ## S3 method for class 'simpr_spec' slice(.data, ..., .preserve = FALSE) ## S3 method for class 'simpr_sims' summarise_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' summarise_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' summarise(.data, ..., .groups = NULL) ## S3 method for class 'simpr_spec' summarise(.data, ..., .groups = NULL) ## S3 method for class 'simpr_sims' tally(x, wt = NULL, sort = FALSE, name = NULL) ## S3 method for class 'simpr_spec' tally(x, wt = NULL, sort = FALSE, name = NULL) ## S3 method for class 'simpr_sims' tbl_vars(x) ## S3 method for class 'simpr_spec' tbl_vars(x) ## S3 method for class 'simpr_sims' transmute_(.data, ..., .dots = list()) ## S3 method for class 'simpr_spec' transmute_(.data, ..., .dots = list()) ## S3 method for class 'simpr_sims' transmute(.data, ...) ## S3 method for class 'simpr_spec' transmute(.data, ...) ## S3 method for class 'simpr_sims' ungroup(x, ...) ## S3 method for class 'simpr_spec' ungroup(x, ...) ## S3 method for class 'simpr_sims' union_all(x, y, ...) ## S3 method for class 'simpr_spec' union_all(x, y, ...) ## S3 method for class 'simpr_sims' union(x, y, ...) ## S3 method for class 'simpr_spec' union(x, y, ...) ## S3 method for class 'simpr_sims' complete_(data, cols, fill = list(), ...) ## S3 method for class 'simpr_spec' complete_(data, cols, fill = list(), ...) ## S3 method for class 'simpr_sims' complete(data, ..., fill = list()) ## S3 method for class 'simpr_spec' complete(data, ..., fill = list()) ## S3 method for class 'simpr_sims' drop_na_(data, vars) ## S3 method for class 'simpr_spec' drop_na_(data, vars) ## S3 method for class 'simpr_sims' drop_na(data, ...) ## S3 method for class 'simpr_spec' drop_na(data, ...) ## S3 method for class 'simpr_sims' expand_(data, dots, ...) ## S3 method for class 'simpr_spec' expand_(data, dots, ...) ## S3 method for class 'simpr_sims' expand(data, ..., .name_repair = "check_unique") ## S3 method for class 'simpr_spec' expand(data, ..., .name_repair = "check_unique") ## S3 method for class 'simpr_sims' extract_( data, col, into, regex = "([[:alnum:]]+)", remove = TRUE, convert = FALSE, ... ) ## S3 method for class 'simpr_spec' extract_( data, col, into, regex = "([[:alnum:]]+)", remove = TRUE, convert = FALSE, ... ) ## S3 method for class 'simpr_sims' extract( data, col, into, regex = "([[:alnum:]]+)", remove = TRUE, convert = FALSE, ... ) ## S3 method for class 'simpr_spec' extract( data, col, into, regex = "([[:alnum:]]+)", remove = TRUE, convert = FALSE, ... ) ## S3 method for class 'simpr_sims' fill_(data, fill_cols, .direction = c("down", "up")) ## S3 method for class 'simpr_spec' fill_(data, fill_cols, .direction = c("down", "up")) ## S3 method for class 'simpr_sims' fill(data, ..., .direction = c("down", "up", "downup", "updown")) ## S3 method for class 'simpr_spec' fill(data, ..., .direction = c("down", "up", "downup", "updown")) ## S3 method for class 'simpr_sims' gather_( data, key_col, value_col, gather_cols, na.rm = FALSE, convert = FALSE, factor_key = FALSE ) ## S3 method for class 'simpr_spec' gather_( data, key_col, value_col, gather_cols, na.rm = FALSE, convert = FALSE, factor_key = FALSE ) ## S3 method for class 'simpr_sims' gather( data, key = "key", value = "value", ..., na.rm = FALSE, convert = FALSE, factor_key = FALSE ) ## S3 method for class 'simpr_spec' gather( data, key = "key", value = "value", ..., na.rm = FALSE, convert = FALSE, factor_key = FALSE ) ## S3 method for class 'simpr_sims' nest_legacy(data, ..., .key = "data") ## S3 method for class 'simpr_spec' nest_legacy(data, ..., .key = "data") ## S3 method for class 'simpr_sims' nest(.data, ..., .names_sep = NULL, .key = lifecycle::deprecated()) ## S3 method for class 'simpr_spec' nest(.data, ..., .names_sep = NULL, .key = lifecycle::deprecated()) ## S3 method for class 'simpr_sims' pivot_longer( data, cols, names_to = "name", names_prefix = NULL, names_sep = NULL, names_pattern = NULL, names_ptypes = list(), names_transform = list(), names_repair = "check_unique", values_to = "value", values_drop_na = FALSE, values_ptypes = list(), values_transform = list(), ... ) ## S3 method for class 'simpr_spec' pivot_longer( data, cols, names_to = "name", names_prefix = NULL, names_sep = NULL, names_pattern = NULL, names_ptypes = list(), names_transform = list(), names_repair = "check_unique", values_to = "value", values_drop_na = FALSE, values_ptypes = list(), values_transform = list(), ... ) ## S3 method for class 'simpr_sims' pivot_wider( data, id_cols = NULL, id_expand = FALSE, names_from = NULL, names_prefix = "", names_sep = "_", names_glue = NULL, names_sort = FALSE, names_vary = "fastest", names_expand = FALSE, names_repair = "check_unique", values_from = NULL, values_fill = NULL, values_fn = NULL, unused_fn = NULL, ... ) ## S3 method for class 'simpr_spec' pivot_wider( data, id_cols = NULL, id_expand = FALSE, names_from = NULL, names_prefix = "", names_sep = "_", names_glue = NULL, names_sort = FALSE, names_vary = "fastest", names_expand = FALSE, names_repair = "check_unique", values_from = NULL, values_fill = NULL, values_fn = NULL, unused_fn = NULL, ... ) ## S3 method for class 'simpr_sims' replace_na(data, replace, ...) ## S3 method for class 'simpr_spec' replace_na(data, replace, ...) ## S3 method for class 'simpr_sims' separate_( data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ... ) ## S3 method for class 'simpr_spec' separate_( data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ... ) ## S3 method for class 'simpr_sims' separate_rows_(data, cols, sep = "[^[:alnum:].]+", convert = FALSE) ## S3 method for class 'simpr_spec' separate_rows_(data, cols, sep = "[^[:alnum:].]+", convert = FALSE) ## S3 method for class 'simpr_sims' separate_rows(data, ..., sep = "[^[:alnum:].]+", convert = FALSE) ## S3 method for class 'simpr_spec' separate_rows(data, ..., sep = "[^[:alnum:].]+", convert = FALSE) ## S3 method for class 'simpr_sims' separate( data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ... ) ## S3 method for class 'simpr_spec' separate( data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ... ) ## S3 method for class 'simpr_sims' spread_( data, key_col, value_col, fill = NA, convert = FALSE, drop = TRUE, sep = NULL ) ## S3 method for class 'simpr_spec' spread_( data, key_col, value_col, fill = NA, convert = FALSE, drop = TRUE, sep = NULL ) ## S3 method for class 'simpr_sims' spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE, sep = NULL) ## S3 method for class 'simpr_spec' spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE, sep = NULL) ## S3 method for class 'simpr_sims' unite_(data, col, from, sep = "_", remove = TRUE) ## S3 method for class 'simpr_spec' unite_(data, col, from, sep = "_", remove = TRUE) ## S3 method for class 'simpr_sims' unite(data, col, ..., sep = "_", remove = TRUE, na.rm = FALSE) ## S3 method for class 'simpr_spec' unite(data, col, ..., sep = "_", remove = TRUE, na.rm = FALSE) ## S3 method for class 'simpr_sims' unnest_legacy(data, ..., .drop = NA, .id = NULL, .sep = NULL, .preserve = NULL) ## S3 method for class 'simpr_spec' unnest_legacy(data, ..., .drop = NA, .id = NULL, .sep = NULL, .preserve = NULL) ## S3 method for class 'simpr_sims' unnest( data, cols, ..., keep_empty = FALSE, ptype = NULL, names_sep = NULL, names_repair = "check_unique", .drop = lifecycle::deprecated(), .id = lifecycle::deprecated(), .sep = lifecycle::deprecated(), .preserve = lifecycle::deprecated() ) ## S3 method for class 'simpr_spec' unnest( data, cols, ..., keep_empty = FALSE, ptype = NULL, names_sep = NULL, names_repair = "check_unique", .drop = lifecycle::deprecated(), .id = lifecycle::deprecated(), .sep = lifecycle::deprecated(), .preserve = lifecycle::deprecated() )
x |
See original function documentation |
... |
See original function documentation |
wt |
See original function documentation |
sort |
See original function documentation |
name |
See original function documentation |
.drop |
See original function documentation |
y |
See original function documentation |
by |
See original function documentation |
copy |
See original function documentation |
.data |
See original function documentation |
.dots |
See original function documentation |
.by_group |
See original function documentation |
.keep_all |
See original function documentation |
data |
See original function documentation |
cols |
See original function documentation |
template |
See original function documentation |
i |
See original function documentation |
.preserve |
See original function documentation |
suffix |
See original function documentation |
keep |
See original function documentation |
add |
See original function documentation |
.add |
See original function documentation |
.tbl |
See original function documentation |
.f |
See original function documentation |
.keep |
See original function documentation |
.key |
See original function documentation |
var |
See original function documentation |
.before |
See original function documentation |
.after |
See original function documentation |
.fn |
See original function documentation |
.cols |
See original function documentation |
in_place |
See original function documentation |
tbl |
See original function documentation |
size |
See original function documentation |
replace |
See original function documentation |
weight |
See original function documentation |
.env |
See original function documentation |
n |
See original function documentation |
prop |
See original function documentation |
order_by |
See original function documentation |
with_ties |
See original function documentation |
weight_by |
See original function documentation |
.groups |
See original function documentation |
fill |
See original function documentation |
vars |
See original function documentation |
dots |
See original function documentation |
.name_repair |
See original function documentation |
col |
See original function documentation |
into |
See original function documentation |
regex |
See original function documentation |
remove |
See original function documentation |
convert |
See original function documentation |
fill_cols |
See original function documentation |
.direction |
See original function documentation |
key_col |
See original function documentation |
value_col |
See original function documentation |
gather_cols |
See original function documentation |
na.rm |
See original function documentation |
factor_key |
See original function documentation |
key |
See original function documentation |
value |
See original function documentation |
.names_sep |
See original function documentation |
names_to |
See original function documentation |
names_prefix |
See original function documentation |
names_sep |
See original function documentation |
names_pattern |
See original function documentation |
names_ptypes |
See original function documentation |
names_transform |
See original function documentation |
names_repair |
See original function documentation |
values_to |
See original function documentation |
values_drop_na |
See original function documentation |
values_ptypes |
See original function documentation |
values_transform |
See original function documentation |
id_cols |
See original function documentation |
id_expand |
See original function documentation |
names_from |
See original function documentation |
names_glue |
See original function documentation |
names_sort |
See original function documentation |
names_vary |
See original function documentation |
names_expand |
See original function documentation |
values_from |
See original function documentation |
values_fill |
See original function documentation |
values_fn |
See original function documentation |
unused_fn |
See original function documentation |
sep |
See original function documentation |
extra |
See original function documentation |
drop |
See original function documentation |
from |
See original function documentation |
.id |
See original function documentation |
.sep |
See original function documentation |
keep_empty |
See original function documentation |
ptype |
See original function documentation |
See original function documentation for details
of the functions. Two methods have been created
for each tidyr and dplyr generic
function: one for simpr_spec objects
(generated by
specify and
define) which are simply stored
for later evaluation by
generate,
and one for simpr_sims objects to
perform the operation elementwise on each
simulation.
To use these special per-simulation versions of
these tidyverse verbs as a part of a
simpr simulation, first run
per_sim on the object. If you do
NOT want to compute only on each simulation but
want to return to the default behavior of
operating on the entire simulation tibble, use
whole_tibble.
simpr_sims methods return a
simpr_sims object with the given data
transformation applied to each simulation.
simpr_spec methods return a simpr_spec
object that stores the given data
transformation, to be executed when
generate
is called.
Undoes per_sim to allow access to simulation
results as a tibble, with simulations available
as a list-column.
whole_tibble(x)whole_tibble(x)
x |
A |
This function is the inverse of
per_sim. This enables tidyverse
verbs to return to the default behavior of
acting on the full table, as opposed to the
behavior, activated by per_sim,
of acting elementwise on the simulation
results.
A tibble with the metaparameters and simulation results