Package {simglm}

Type: Package
Version: 1.0.0
Date: 2026-09-01
License: MIT + file LICENSE
Title: Simulate Models Based on the Generalized Linear Model
Description: Simulates regression models, including both simple regression and generalized linear mixed models with up to three level of nesting. Power simulations that are flexible allowing the specification of missing data, unbalanced designs, and different random error distributions are built into the package.
Depends: R (≥ 4.1.0)
Imports: stats, methods, rlang, dplyr, purrr, broom, future.apply, gtools, sandwich, lmtest, splines
Suggests: knitr, lme4, nlme, testthat, shiny, e1071, ggplot2, tidyr, geepack, rmarkdown, future, covr
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 7.3.3
Author: Brandon LeBeau [aut, cre]
Maintainer: Brandon LeBeau <lebebr01+simglm@gmail.com>
URL: https://github.com/lebebr01/simglm
BugReports: https://github.com/lebebr01/simglm/issues
NeedsCompilation: no
Packaged: 2026-09-01 19:54:33 UTC; blebeau
Repository: CRAN
Date/Publication: 2026-09-01 20:20:02 UTC

simglm: Simulate Models Based on the Generalized Linear Model

Description

Simulates regression models, including both simple regression and generalized linear mixed models with up to three level of nesting. Power simulations that are flexible allowing the specification of missing data, unbalanced designs, and different random error distributions are built into the package.

Author(s)

Maintainer: Brandon LeBeau lebebr01+simglm@gmail.com

See Also

Useful links:

Aggregate outcome to specified cluster level

Description

Takes individual-level linear predictors and aggregates them to the specified cluster level. This is used for cluster-level treatment assignment in propensity score models.

Usage

aggregate_outcome_by_level(
  outcome,
  data,
  sim_args,
  multinomial_categories = NULL
)

Arguments

outcome

Numeric vector or matrix of linear predictors (untransformed outcomes)

data

Data frame containing cluster ID variables

sim_args

Simulation arguments list containing outcome_level specification

multinomial_categories

Categories for multinomial outcome transformation

Value

Aggregated outcome vector with cluster-level values repeated for all individuals within each cluster

Convenience function for computing density values for plotting.

Description

Convenience function for computing density values for plotting.

Usage

compute_density_values(data, group_var, parameter, values, term_col = "term")

Arguments

data

A dataframe that contains the parameter estimates.

group_var

A group variable that specifies the attributes to group by. By default, this would likely be the term attribute, but can contain more than one attribute.

parameter

The attribute that represents the parameter estimate.

values

A list of numeric vectors that specifies the values for which the density values are computed for.

term_col

A character vector representing the term column in the data, this should almost always be "term" unless renamed.

Compute Power, Type I Error, or Precision Statistics

Description

Compute Power, Type I Error, or Precision Statistics

Usage

compute_statistics(
  data,
  sim_args,
  power = TRUE,
  type_1_error = TRUE,
  precision = TRUE,
  alternative_power = FALSE,
  type_use = FALSE,
  type_m_error = FALSE
)

Arguments

data

A list of model results generated by replicate_simulation function.

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

power

TRUE/FALSE flag indicating whether power should be computed. Defaults to TRUE.

type_1_error

TRUE/FALSE flag indicating whether type I error rate should be computed. Defaults to TRUE.

precision

TRUE/FALSE flag indicating whether precision should be computed. Defaults to TRUE.

alternative_power

TRUE/FALSE flag indicating whether alternative power estimates should be computed. If TRUE, this must be accompanied by thresholds specified within the power simulation arguments. Defaults to FALSE.

type_use

TRUE/FALSE flag indicating whether unconditional sign errors (USE) should be computed. Defaults to FALSE.

type_m_error

TRUE/FALSE flag indicating whether Type M error should be computed. Defaults to FALSE.

Correlate elements

Description

Correlate elements

Usage

correlate_variables(data, sim_args, ...)

Arguments

data

Data simulated from other functions to pass to this function.

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

  • correlate: These are the correlations for random effects and/or fixed effects.

...

Additional arguments, currently not used.

Computes mixture normal variance

Description

Input the desired variance, number of distributions, and mean of the distributions, returns a value of the variance of each mixture distribution.

Usage

desireVar(desVar, num_dist, means, equalWeight = TRUE)

Arguments

desVar

Desired overall variance of mixture normal distribution.

num_dist

Number of normal distributions.

means

Vector of means for each normal distribution. Must equal num_dist.

equalWeight

Should equal weights be used, only TRUE is currently supported.

Details

This function can be used to generate the inputs for the rbimod variances when a specific variance is desired. Especially useful when attempting to simulate a mixture normal/bimodal distribution.

Extract Coefficients

Description

Extract Coefficients

Usage

extract_coefficients(model, extract_function = NULL)

Arguments

model

A returned model object from a fitted model.

extract_function

A function that extracts model results. The function must take the model object as the only argument.

Primary propensity model fitting

Description

Incorporates the estimated probabilities and weights.

Usage

fit_propensity(data, sim_args)

Arguments

data

Data object that contains the simulated data.

sim_args

The simulation argument list that contains the propensity score arguments needed.

Value

Propensity probabilities and weights if specified.

Tidy Missing Data Function

Description

Tidy Missing Data Function

Usage

generate_missing(data, sim_args)

Arguments

data

Data simulated from other functions to pass to this function.

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

Simulate response variable

Description

Simulate response variable

Usage

generate_response(data, sim_args, keep_intermediate = TRUE, ...)

Arguments

data

Data simulated from other functions to pass to this function.

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

keep_intermediate

TRUE/FALSE flag indicating whether intermediate steps should be kept. This would include fixed effects times regression weights, random effect summations, etc. Default is TRUE.

...

Other arguments to pass to error simulation functions.

Missing Data Functions

Description

Function that inputs simulated data and returns data frame with new response variable that includes missing data. Missing data types incorporated include dropout missing data, missing at random, and random missing data.

Usage

missing_data(
  sim_data,
  resp_var = "sim_data",
  new_outcome = "sim_data2",
  clust_var = NULL,
  within_id = NULL,
  miss_prop = NULL,
  dropout_location = NULL,
  type = c("dropout", "random", "mar"),
  miss_cov,
  mar_prop
)

dropout_missing(
  sim_data,
  resp_var = "sim_data",
  new_outcome = "sim_data2",
  clust_var = "clustID",
  within_id = "withinID",
  miss_prop = NULL,
  dropout_location = NULL
)

random_missing(
  sim_data,
  resp_var = "sim_data",
  new_outcome = "sim_data2",
  miss_prop,
  clust_var = NULL,
  within_id = "withinID"
)

mar_missing(
  sim_data,
  resp_var = "sim_data",
  new_outcome = "sim_data2",
  miss_cov,
  mar_prop
)

Arguments

sim_data

Simulated data frame

resp_var

Character string of response variable with complete data.

new_outcome

Character string of new outcome variable name that includes the missing data.

clust_var

Cluster variable used for the grouping, set to NULL by default which means no clustering.

within_id

ID variable within each cluster.

miss_prop

Proportion of missing data overall

dropout_location

A vector the same length as the number of clusters representing the number of data observations for each individual.

type

The type of missing data to generate, currently supports dropout, random, or missing at random (mar) missing data.

miss_cov

Covariate that the missing values are based on.

mar_prop

Proportion of missing data for each unique value specified in the miss_cov argument.

Tidy Model Fitting Function

Description

Tidy Model Fitting Function

Usage

model_fit(data, sim_args, ...)

Arguments

data

A data object, most likely generated from within simglm

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

  • model_fit: These are arguments passed to the model_fit function.

...

Currently not used.

Parse correlation arguments

Description

This function is used to parse user specified correlation attributes. The correlation attributes need to be in a dataframe to be processed internally. Within the dataframe, there are expected to be 3 columns, 1) names of variable/attributes, 2) the variable/attribute pair for 1, 3) the correlation.

Usage

parse_correlation(sim_args)

Arguments

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

  • correlate: These are the correlations for random effects and/or fixed effects.

Parses tidy formula simulation syntax

Description

A function that parses the formula simulation syntax in order to simulate data.

Usage

parse_formula(sim_args)

Arguments

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

Parse Multiple Membership Random Effects

Description

Parse Multiple Membership Random Effects

Usage

parse_multiplemember(sim_args, random_formula_parsed)

Arguments

sim_args

Simulation arguments

random_formula_parsed

This is the output from parse_randomeffect.

Parse power specifications

Description

Parse power specifications

Usage

parse_power(sim_args, samp_size)

Arguments

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

samp_size

The sample size pulled from the simulation arguments or the power model results when vary_arguments is used.

Parses random effect specification

Description

Parses random effect specification

Usage

parse_randomeffect(formula)

Arguments

formula

Random effect formula already parsed by parse_formula

Parse between varying arguments

Description

Parse between varying arguments

Usage

parse_varyarguments(sim_args)

Arguments

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

Parse within varying arguments

Description

Parse within varying arguments

Usage

parse_varyarguments_w(sim_args, name)

Arguments

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

name

The name of the within simulation condition. This is primarily an internal function.

Simulating mixture normal distributions

Description

Input simulation metrics returns mixture normal random variable.

Usage

rbimod(n, mean, var, num_dist)

Arguments

n

Number of random draws. Optionally can be a vector with number in each simulated normal distribution.

mean

Vector of mean values for each normal distribution. Must be the same length as num_dist.

var

Vector of variance values for each normal distribution. Must be the same length as num_dist.

num_dist

Number of normal distributions to use when simulating mixture normal distribution.

Details

Function to simulate mixture normal distributions. The function computes adds the specified number of normal distributions into a single vector.

Use of the function desireVar can be used to generate a mixture normal distribution with a specific global variance.

Replicate Simulation

Description

Replicate Simulation

Usage

replicate_simulation(sim_args, return_list = FALSE, future.seed = TRUE, ...)

Arguments

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

return_list

TRUE/FALSE indicating whether a full list output should be returned. If TRUE, the nested list is returned. If FALSE, replications are combined with a replication id appended.

future.seed

TRUE/FALSE or numeric. Default value is true, see future_replicate.

...

Currently not used.

Robust Model Standard Errors

Description

Robust Model Standard Errors

Usage

robust_model(model, data = NULL, robust_args = list())

Arguments

model

A fitted model object from lm or glm.

data

A data object that contains a fitted model.

robust_args

A named list of arguments passed to the robust model standard error function.

Run Shiny Application Demo

Description

Function runs Shiny Application Demo

Usage

run_shiny()

Details

This function does not take any arguments and will run the Shiny Application. If running from RStudio, will open the application in the viewer, otherwise will use the default internet browser.

Simulate continuous variables

Description

Function that simulates continuous variables. Any distribution function in R is supported.

Usage

sim_continuous2(
  n,
  dist = "rnorm",
  var_level = 1,
  variance = NULL,
  ther_sim = FALSE,
  ther_val = NULL,
  ceiling = NULL,
  floor = NULL,
  ...
)

Arguments

n

A list of sample sizes.

dist

A distribution function. This argument takes a quoted R distribution function (e.g. 'rnorm'). Default is 'rnorm'.

var_level

The level the variable should be simulated at. This can either be 1, 2, or 3 specifying a level 1, level 2, or level 3 variable respectively.

variance

The variance for random effect simulation.

ther_sim

A TRUE/FALSE flag indicating whether the error simulation function should be simulated, that is should the mean and standard deviation used for standardization be simulated.

ther_val

A vector of 2 that should include the theoretical mean and standard deviation of the generating function.

ceiling

A numeric value that specifies the ceiling (maximum) of an attribute being generated. Defaults to NULL meaning no ceiling effect. If a value is specified, any data larger than integer is rounded to that ceiling value.

floor

A numeric value that specifies the floor (minimum) of an attribute being generated. Defaults to NULL meaning no floor effect. If a value is specified, any data larger than integer is rounded to that floor value.

...

Additional parameters to pass to the dist_fun argument.

Simulate categorical or factor variables

Description

Function that simulates factor or categorical variables. Is essentially a wrapper around the sample function from base R.

Usage

sim_factor2(n, levels, var_level = 1, replace = TRUE, force_equal = FALSE, ...)

Arguments

n

A list of sample sizes.

levels

Scalar indicating the number of levels for categorical or factor variable. Can also specify levels as a character vector.

var_level

The level the variable should be simulated at. This can either be 1, 2, or 3 specifying a level 1, level 2, or level 3 variable respectively.

replace

TRUE/FALSE indicating whether levels should be sampled with replacement. Default is TRUE.

force_equal

TRUE/FALSE indicating if the sample size should be forced to be equal. Should not be used with the 'replace = FALSE' argument.

...

Additional parameters passed to the sample function.

Simulate discrete variables

Description

Function that simulates discrete variables. Is essentially a wrapper around the sample function from base R.

Usage

sim_ordinal2(n, levels, var_level = 1, replace = TRUE, ...)

Arguments

n

A list of sample sizes.

levels

Scalar indicating the number of levels for discrete variable. Can also specify levels as a character vector.

var_level

The level the variable should be simulated at. This can either be 1, 2, or 3 specifying a level 1, level 2, or level 3 variable respectively.

replace

TRUE/FALSE indicating whether levels should be sampled with replacement. Default is TRUE.

...

Additional parameters passed to the sample function.

Simulate Time

Description

This function simulates data for the time variable of longitudinal data.

Usage

sim_time(n, time_levels = NULL, ...)

Arguments

n

Sample size of the levels.

time_levels

The values the time variable should take. If NULL (default), the time values are discrete integers starting at 0 and going to n - 1.

...

Currently not used.

Single wrapper function

Description

This function is most useful to pass to replicate_simulation. The function attempts to determine automatically which aspects to add to the simulation/power generation based on the elements found in the sim_args argument.

Usage

simglm(sim_args)

Arguments

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

Tidy error simulation

Description

Tidy error simulation

Usage

simulate_error(data, sim_args, ...)

Arguments

data

Data simulated from other functions to pass to this function.

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

...

Other arguments to pass to error simulation functions.

Tidy fixed effect formula simulation

Description

This function simulates the fixed portion of the model using a formula syntax.

Usage

simulate_fixed(data, sim_args, ...)

Arguments

data

Data simulated from other functions to pass to this function. Can pass NULL if first in simulation string.

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

...

Other arguments to pass to error simulation functions.

Tidy heterogeneity of variance simulation

Description

This function simulates heterogeneity of level one error variance.

Usage

simulate_heterogeneity(data, sim_args, ...)

Arguments

data

Data simulated from other functions to pass to this function. This function needs to be specified after 'simulate_fixed' and 'simulate_error'.

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

...

Other arguments to pass to error simulation functions.

Simulate knot locations

Description

Function that generates knot locations. An example of usefulness of this function would be with generation of interrupted time series data. Another application may be with simulation of piecewise linear data structures.

Usage

simulate_knot(data, sim_args)

Arguments

data

Mostly internal argument.

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

Simulate Propensity Scores

Description

Process to simulate propensity scores.

Usage

simulate_propensity(sim_args)

Arguments

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

Tidy random effect formula simulation

Description

This function simulates the random portion of the model using a formula syntax.

Usage

simulate_randomeffect(data, sim_args, ...)

Arguments

data

Data simulated from other functions to pass to this function. Can pass NULL if first in simulation string.

sim_args

A named list with special model formula syntax. See details and examples for more information. The named list may contain the following:

  • fixed: This is the fixed portion of the model (i.e. covariates)

  • random: This is the random portion of the model (i.e. random effects)

  • error: This is the error (i.e. residual term).

...

Other arguments to pass to error simulation functions.

Transform response variable

Description

Transform response variable

Usage

transform_outcome(outcome, type, categories = NULL, ...)

Arguments

outcome

The outcome variable to transform.

type

Type of transformation to apply.

categories

A vector of named categories for multinomial sim

...

Additional arguments passed to distribution functions.