Create a SIMULATE specification for use in rblimp()

Description

Creates a simulation specification that can be passed to rblimp() as the data argument. Instead of reading existing data, rblimp() will use Blimp's SIMULATE command to generate data and then fit the specified model to it.

Usage

SIMULATE(model, n, define = NULL, variables = NULL)

Arguments

Value

A blimp_simulate object (subclass of blimp_syntax) that can be passed to rblimp() as the data argument.

See Also

rblimp_sim() for directly generating simulated data without fitting a model

Examples

# Create simulation specification
sim_spec <- SIMULATE(
  model = c(
    "x = normal(0, 1)",
    "y = normal(10 + x*0.5, 1)"
  ),
  n = 1000
)

# View the specification
print(sim_spec)

# Use in rblimp to fit a model to simulated data
mdl <- rblimp(
  model = "y ~ x",
  data = sim_spec,
  seed = 123,
  burn = 5000,
  iter = 5000
)

summary(mdl)

Extract algorithmic options from Blimp output

Description

Extracts the algorithmic options section from Blimp output.

Usage

algorithm(object)

Arguments

Value

A blimp_out object containing algorithmic options

Convert blimp_syntax to character vector

Description

Convert blimp_syntax to character vector

Usage

## S3 method for class 'blimp_syntax'
as.character(x, ...)

Arguments

Value

A character string containing the formatted 'Blimp' input syntax with a header comment and all commands.

Convert blimp_obj to data.frame

Description

Convert blimp_obj to data.frame

Usage

## S4 method for signature 'blimp_obj'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Value

A data.frame containing the MCMC iterations from the model.

Convert blimp_obj to matrix

Description

Convert blimp_obj to matrix

Usage

## S4 method for signature 'blimp_obj'
as.matrix(x, ...)

Arguments

Value

A numeric matrix containing the MCMC iterations from the model.

Coerces a blimp_obj or blimp_bygroup to a mitml.list

Description

Coerces a blimp_obj or blimp_bygroup to a mitml.list

Usage

as.mitml(object)

Arguments

Value

A list of class "mitml.list" containing the imputed data sets, suitable for use with functions from the 'mitml' package.

S4 class for Blimp model comparison results

Description

Result object from comparing two Blimp models.

Slots

table

Matrix of comparison results

model0

The reference blimp_obj model

model

The comparison blimp_obj model

greaterThan

Logical indicating direction of comparison

S4 class for Blimp model results

Description

The main result object containing Blimp model estimates, iterations, and output.

Slots

call

The function call that created the object

estimates

Matrix of parameter estimates

burn

List of burn-in information for each chain

iterations

Data frame of MCMC iterations

psr

Data frame of Potential Scale Reduction values

imputations

List of imputed datasets

average_imp

Data frame of average imputations

variance_imp

Data frame of imputation variances

waldtest

Data frame of Wald test results

simple

Data frame for simple slopes analysis

syntax

The blimp_syntax object used to run the model

output

The blimp_out object containing raw output text

Wrapper function to run multiple instances of rblimp based on grouping variable

Description

rblimp will generate the input, run the script, and load most the saved data into an R object. rblimp_fcs is used to specify the FCS command in Blimp. rblimp_syntax will generate the Blimp syntax file only.

Usage

by_group(expr, group)

Arguments

Details

Separates by grouping to run blimp command on each individual sub data set.

Value

a list of all blimp runs by group with class of blimp_bygroup

See Also

as.mitml

Examples

# Generate Data
mydata <- rblimp_sim(
    c(
        'x ~ normal(0, 1)',
        'y ~ normal(0, 1)',
        'group = uniform(0, 1) < 0.5'
    ),
    n = 1000,
    seed = 10972
)

# Nonsensical model
mdl <- rblimp_fcs(
    c('x', 'y'),
    mydata,
    seed = 3927,
    nimps = 2,
) |> by_group('group')

# Analyze each imputation
results <- with(mdl, lm(y ~ x))
# use `mitml` package to pool results

Compare two Blimp models

Description

Compares two Bayesian models by calculating the proportion of posterior samples where the comparison model's parameters exceed (or fall below) the reference model's summary statistic. This is useful for model comparison and assessing incremental variance explained (e.g., R-squared differences).

Usage

compare(
  model0,
  model,
  use = "mean",
  greaterThan = TRUE,
  suffixes = c("R2: Coefficients", "R2: Level-2 Random Intercepts",
    "R2: Level-2 Random Slopes", "R2: Level-3 Random Slopes",
    "R2: Level-3 Random Intercepts", "R2: Residual Variation",
    "R2: Level-1 Residual Variation")
)

Arguments

Details

The comparison works by:

1. Computing a summary statistic (e.g., mean) from model0's posterior samples

2. Calculating what proportion of model's posterior samples exceed this value

3. Reporting this proportion for each parameter matching the specified suffixes

Value

A blimp_cp object containing a matrix of comparison proportions.

Note

Due to R restrictions, lists of functions will not give useful printed names.

Examples

# Generate data
mydata <- rblimp_sim(
    c(
        'x1 ~ normal(0, 1)',
        'x2 ~ normal(0, 1)',
        'y ~ normal(10 + 0.5*x1 + 0.3*x2, 1)'
    ),
    n = 200,
    seed = 123
)

# Fit baseline model (x1 only)
model0 <- rblimp(
    'y ~ x1',
    mydata,
    seed = 123,
    burn = 1000,
    iter = 1000
)

# Fit comparison model (x1 + x2)
model1 <- rblimp(
    'y ~ x1 x2',
    mydata,
    seed = 123,
    burn = 1000,
    iter = 1000
)

# Compare models - proportion of model1 R-squared > mean(model0 R-squared)
compare(model0, model1)

Convenience Function for computing conditional effects for jn_plot_func

Description

Convenience Function for computing conditional effects for jn_plot_func

Usage

compute_condeff(value1, value2)

Arguments

Value

a function

See Also

jn_plot_func()

Examples

# Generate Data
mydata <- rblimp_sim(
    c(
        'x1 ~ normal(0, 1)',
        'x2 ~ normal(0, 1)',
        'm ~ normal(0, 1)',
        'y ~ normal(10 + 0.5*x1 + 0.5*x2 + m + 0.2*x1*x2 + 0.3*x2*m + 0.1*x1*m + 0.7*x1*x2*m, 1)'
    ),
    n = 100,
    seed = 981273
)

# Run Rblimp
m1 <- rblimp(
    'y ~ x1 x2 m x1*x2 x1*m x2*m x1*x2*m',
    mydata,
    center = ~ m,
    seed = 10972,
    burn = 1000,
    iter = 1000
)

# Get parameter values
params <- m1 |> as.matrix()

# Generate Plot
(
    jn_plot_func(
        compute_condeff(params[,6], params[,9]),
        xrange = c(-3, 3)
    )
    # Set custom colors
    + ggplot2::scale_fill_manual(
        values = c(`FALSE` = '#ca0020', `TRUE` = '#0571b0')
    )
    + ggplot2::labs(
        title = 'Johnson-Neyman Plot for `x1` * `x2` Moderated by `x2`',
        subtitle = 'Red area represents 0 within 95% interval',
        y = 'y ~ x1 * x2',
        x = 'm'
    )
    + ggplot2::theme_minimal()
)

Extract data information from Blimp output

Description

Extracts the data information section from Blimp output.

Usage

datainfo(object)

Arguments

Value

A blimp_out object containing data information

Describe an object

Description

describe provides a flexible summary of various R objects, such as data frames or Blimp model objects. It is an alternative to summary and is inspired by precis from the rethinking package.

Usage

describe(
  object,
  depth = 1,
  pars,
  prob = 0.95,
  digits = 2,
  sort = NULL,
  decreasing = FALSE,
  ...
)

## S4 method for signature 'data.frame'
describe(
  object,
  depth = 1,
  pars,
  prob = 0.5,
  digits = 2,
  sort = NULL,
  decreasing = FALSE,
  hist = TRUE,
  ...
)

## S4 method for signature 'blimp_obj'
describe(
  object,
  depth = 1,
  pars,
  prob = 0.95,
  digits = 2,
  sort = NULL,
  decreasing = FALSE,
  hist = TRUE,
  ...
)

Arguments

Value

An object of class describe, which is a data frame containing summary statistics and attributes for printing.

Author(s)

The data.frame method is adapted from Richard McElreath's precis function in the rethinking package (https://github.com/rmcelreath/rethinking). The histospark function is by Hadley Wickham (GPL-3). The blimp_obj method was written for this package.

The 'describe' class for object summaries

Description

An S4 class that holds a summary data frame and formatting information. It extends the data.frame class and is the return type for the describe() function.

Usage

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

Arguments

Slots

digits

A numeric value indicating the number of digits to display.

Produce Blimp location Conditional on Operating System

Description

This function is called when running Blimp and can be used to determine what location rblimp uses for the Blimp executable.

Usage

detect_blimp()

Details

rblimp will first use any location set with the set_blimp function. Next, it will check if R_BLIMP is set in the environment variables. Finally, it will fall back to the default Blimp install location based on the operating system.

Value

A character string of blimp's executable location.

See Also

set_blimp to set blimp location

Examples

# Obtain blimp location
detect_blimp()

Extract model estimates from Blimp output

Description

Extracts the model estimates section from Blimp output.

Usage

estimates(object)

Arguments

Value

A blimp_out object containing model estimates

Does the system have blimp installed?

Description

Returns a logical if blimp is detected by detect_blimp

Usage

has_blimp()

Details

This function uses detect_blimp to determine if blimp is installed.

Value

a logical

See Also

detect_blimp

Examples

# Detect if system has blimp
has_blimp()

Function to generate a Johnson-Neyman Plot of Conditional Slope with rblimp

Description

Generates a Johnson-Neyman Plot based on the posterior summaries from the output of rblimp.

Usage

jn_plot(formula, model, ci = 0.95, ...)

Arguments

Details

To change colors use ggplot2's scale system. Both fill and color are used. See ggplot2::aes_colour_fill_alpha for more information about setting a manual set of colors.

Value

a ggplot2::ggplot plot. The bounding values are saved in the attribute 'bounds'.

See Also

jn_plot_func()

Examples

# Generate Data
mydata <- rblimp_sim(
    c(
        'x ~ normal(0, 1)',
        'm ~ normal(0, 1)',
        'y ~ normal(10 + 0.5*x + m + 0.2*x*m, 1)'
    ),
    n = 100,
    seed = 981273
)

# Run Rblimp
m1 <- rblimp(
    'y ~ x m x*m',
    mydata,
    center = ~ m,
    simple = 'x | m',
    seed = 10972,
    burn = 1000,
    iter = 1000
)

# Generate Plot
jn_plot(y ~ x | m, m1)

# Generate Plot with different colors
(
    jn_plot(y ~ x | m, m1)
    + ggplot2::scale_fill_manual(
        values = c(`FALSE` = '#ca0020', `TRUE` = '#0571b0')
    )
)

Function to generate a Johnson-Neyman Plot of Conditional Slope based on a function to produce the conditional effect.

Description

Generates a Johnson-Neyman Plot using a function to produce the conditional effect

Usage

jn_plot_func(func, xrange, ci = 0.95, ...)

Arguments

Details

To change colors use ggplot2's scale system. Both fill and color are used. See ggplot2::aes_colour_fill_alpha for more information about setting a manual set of colors.

For ..., the arguments are passed to the internal boundary search algorithm. This algorithm uses an initial grid search to locate boundaries based on the range and then a binary search to refine the estimates. The following arguments are available:

n_initial

Number of points in the initial coarse grid search used to locate approximate boundary positions. Higher values improve detection of closely-spaced boundaries but increase computation time. Default is 1000.

refine_tol

Tolerance for binary search refinement. The algorithm refines each boundary until the interval width is smaller than this value. Smaller values give higher precision but require more function evaluations. Default is 1e-12.

max_iter

Maximum number of iterations for binary search refinement per boundary. Prevents infinite loops if tolerance cannot be achieved. Default is 100.

adaptive

Logical indicating whether to perform additional refinement in regions where boundaries are detected to be closely spaced. When TRUE, uses a finer grid to resolve boundaries that may be missed by the initial coarse grid. Default is TRUE.

Value

a ggplot2::ggplot plot. The bounding values are saved in the attribute 'bounds'.

See Also

compute_condeff()

Examples

# Generate Data
mydata <- rblimp_sim(
    c(
        'x1 ~ normal(0, 1)',
        'x2 ~ normal(0, 1)',
        'm ~ normal(0, 1)',
        'y ~ normal(10 + 0.5*x1 + 0.5*x2 + m + 0.2*x1*x2 + 0.3*x2*m + 0.1*x1*m + 0.7*x1*x2*m, 1)'
    ),
    n = 100,
    seed = 981273
)

# Run Rblimp
m1 <- rblimp(
    'y ~ x1 x2 m x1*x2 x1*m x2*m x1*x2*m',
    mydata,
    center = ~ m,
    seed = 10972,
    burn = 1000,
    iter = 1000
)

# Get parameter values
params <- m1 |> as.matrix()

# Generate Plot
(
    jn_plot_func(
        compute_condeff(params[,6], params[,9]),
        xrange = c(-3, 3)
    )
    + ggplot2::labs(
        title = 'Johnson-Neyman Plot for `x1` * `x2` Moderated by `x2`',
        subtitle = 'Red area represents 0 within 95% interval',
        y = 'y ~ x1 * x2',
        x = 'm'
    )
    + ggplot2::theme_minimal()
)

Create APA-formatted table of model results

Description

Generates an APA-formatted HTML table of model estimates that opens in the RStudio Viewer. Provides publication-ready tables with proper formatting for Bayesian model results.

Usage

model_table(
  object,
  selector,
  digits = 3,
  caption = NULL,
  show_chains = TRUE,
  show_neff = FALSE
)

Arguments

Details

The function creates APA-style tables with the following features:

• Proper coefficient formatting with confidence intervals

• Organized sections for different parameter types

• Publication-ready styling

• Responsive design for different screen sizes

Tables are displayed in the RStudio Viewer pane and can be exported or copied.

Value

Invisibly returns the HTML content. Primary purpose is displaying in Viewer.

Examples

# Generate Data
mydata <- rblimp_sim(
    c(
        'x ~ normal(0, 1)',
        'y ~ normal(10 + 0.5*x, 1)'
    ),
    n = 100,
    seed = 10972
)

# Fit model
model <- rblimp(
    'y ~ x',
    mydata,
    seed = 10972,
    burn = 1000,
    iter = 1000
)

# Create APA table for all variables
model_table(model)

# Create table for specific variable
model_table(model, "y")

# Customize formatting
model_table(model, digits = 2, caption = "Bayesian Model Results")

Extract model fit from Blimp output

Description

Extracts the model fit section from Blimp output.

Usage

modelfit(object)

Arguments

Value

A blimp_out object containing model fit information

Extract model information from Blimp output

Description

Extracts the model information section from Blimp output.

Usage

modelinfo(object)

Arguments

Value

A blimp_out object containing model information

Return variable names from blimp_obj object

Description

Return variable names from blimp_obj object

Usage

## S4 method for signature 'blimp_obj'
names(x)

Arguments

Value

A character vector of variable names from the average imputation data.

Extract output from blimp_obj

Description

Extracts the raw Blimp output text from a blimp_obj.

Usage

output(object)

Arguments

Value

A blimp_out object containing the raw Blimp output text

Function to generate graph posterior density plots for parameters

Description

Generates ggplot2::ggplot plots using ggplot2::geom_density based on the output from rblimp

Usage

posterior_plot(model, selector, ...)

Arguments

Details

To change colors use ggplot2's scale system. Both fill and color are used. See ggplot2::aes_colour_fill_alpha for more information about setting a manual set of colors.

Value

a ggplot2::ggplot plot

Examples

# Generate Data
mydata <- rblimp_sim(
    c(
        'x ~ normal(0, 1)',
        'm ~ normal(0, 1)',
        'y ~ normal(10 + 0.5*x + m + 0.2*x*m, 1)'
    ),
    n = 100,
    seed = 981273
)

# Run Rblimp
m1 <- rblimp(
    c(
        'y ~ x m',
        'x ~~ m'
    ),
    mydata,
    seed = 10972,
    burn = 1000,
    iter = 1000
)

# Generate plot of all parameters with `y`
posterior_plot(m1, 'y') + ggplot2::theme_minimal()

# Generate plot of all parameters
posterior_plot(m1) + ggplot2::theme_minimal()

# Generate plot of all parameters for `y` and `x`
posterior_plot(m1, c('x', 'y')) + ggplot2::theme_minimal()
# Generate Plot of Parameter 5
posterior_plot(m1, 5) + ggplot2::theme_minimal()

# Generate plot of `x residual variance`
posterior_plot(m1, 'x residual variance') + ggplot2::theme_minimal()

# Generate plot of Parameters 7 and 9
posterior_plot(m1, c(7, 9)) + ggplot2::theme_minimal()

Predicted scores from blimp_obj

Description

Predicted scores from blimp_obj

Usage

## S4 method for signature 'blimp_obj'
predict(object, ...)

Arguments

Value

A list of data frames, one per imputation, each containing the predicted values and probability columns from the model.

Print blimp_syntax

Description

Print blimp_syntax

Usage

## S3 method for class 'blimp_syntax'
print(x, ...)

Arguments

Value

The blimp_syntax object (invisibly). Called for its side effect of printing the formatted syntax to the console.

Extract Potential Scale Reduction (PSR) output

Description

Extracts the PSR convergence diagnostic section from Blimp output.

Usage

psr(object)

Arguments

Value

A blimp_out object containing PSR output

Wrapper functions to provide Blimp functions in R

Description

rblimp will generate the input, run the script, and load most the saved data into an R object. rblimp_fcs is used to specify the FCS command in Blimp. rblimp_syntax will generate the Blimp syntax file only.

Usage

rblimp(
  model,
  data,
  burn = 10000,
  iter = 10000,
  seed,
  thin,
  nimps,
  latent,
  randomeffect,
  parameters,
  clusterid,
  timeid,
  weight,
  ordinal,
  nominal,
  count,
  center,
  chains,
  simple,
  waldtest,
  options,
  transform,
  dropout,
  filter,
  fixed,
  output,
  tmpfolder,
  add_save = TRUE,
  print_output = TRUE,
  nopowershell = FALSE
)

rblimp_fcs(
  variables,
  data,
  burn = 10000,
  iter = 10000,
  seed,
  thin,
  nimps,
  clusterid,
  ordinal,
  nominal,
  chains,
  options,
  transform,
  fixed,
  output,
  tmpfolder,
  add_save = TRUE,
  print_output = TRUE,
  nopowershell = FALSE
)

rblimp_syntax(
  model,
  data,
  burn,
  iter,
  seed,
  thin,
  nimps,
  latent,
  randomeffect,
  parameters,
  clusterid,
  timeid,
  weight,
  ordinal,
  nominal,
  count,
  transform,
  dropout,
  filter,
  fixed,
  center,
  chains,
  simple,
  waldtest,
  options,
  save,
  output
)

Arguments

Details

The above functions require knowledge of specifying Blimp commands. Blimp's syntax commands are documented in the Blimp User Manual

By default, these commands can be inputted as character strings that will be used to generate the syntax. For multiple lined commands, you can wrap multiple strings into a character vector or a list. The appropriate semicolons will be specified, so they are not required in any character strings. If specifying a character vector or a list for the model, providing names to each element will be used as blocks in Blimp's model syntax. Similarly, specifying named elements can be used for the center command to specify if you would like centering within a cluster or grand mean centering. This also works for the latent input when requesting latent variables at a specific cluster identifier. See the Blimp User Manual for more details about types of centering and specifying latent variables.

In addition, R's formula syntax can be used to specify lists of variables in place of character strings. For example, specifying the CLUSTERID command with the variable id can be specified as ~ id as opposed to a character string. Similarly, + can be used to specify multiple variables. For example, to specify two variables as ordinal the formula would be: ~ x1 + x2. Finally, this can also be used to specify centering and latent variables. For example, to center x1 and x2 within cluster we can specify: cwc ~ x1 + x2.

Running rblimp will also check if blimp is up to date. See details in rblimp_source for more information.

Value

blimp_obj

See Also

• SIMULATE() for creating simulated data to fit within rblimp()

• rblimp_sim() for generating simulated datasets

Examples

# Generate Data with `rblimp_sim`
mydata <- rblimp_sim(
    c(
        'f ~ normal(0, 1)',
        'x1:x5 ~ normal(f, 1)',
        'y ~ normal(10 + 0.3*f, 1 - .3^2)'
     ),
     n = 500,
     seed = 19723,
     variables = c('y', 'x1:x5')
)

# Fit SEM Model
model <- rblimp(
    list(
        structure = 'y ~ f',
        measurement = 'f -> x1:x5'
    ),
    mydata,
    seed = 3927,
    latent = ~ f
)

# View results
summary(model)

Getting Started with rblimp

Description

A guide to installing and configuring Blimp software for use with rblimp.

Installing Blimp Software

Before using rblimp, you must install the Blimp software (freely available):

1. Visit https://www.appliedmissingdata.com/blimp

2. Download the version for your operating system

3. Follow the installation instructions

After installation, configure Blimp. The package will look for Blimp in the following order:

1. The R_BLIMP environment variable

2. Common installation locations (automatic detection)

To configure Blimp:

# Automatic detection
detect_blimp()

# Or set path manually (for current session)
set_blimp("/path/to/blimp")

# Or set R_BLIMP environment variable (permanent)
# Add to .Renviron file:
# R_BLIMP="/path/to/blimp"

# Verify installation
has_blimp()

Automatic Update Checks

By default, rblimp automatically checks for Blimp updates when you run models. This ensures you're using the latest version with bug fixes and improvements.

To disable automatic update checks:

# Disable for current session
options(check_blimp_update = FALSE)

# Or add to .Rprofile for permanent setting:
# options(check_blimp_update = FALSE)

You can manually check for updates at any time:

update_blimp()

Function Reference

Model Fitting:

• rblimp - Fit Bayesian models

• rblimp_fcs - Fully Conditional Specification imputation

• rblimp_syntax - Generate Blimp syntax

• rblimp_source - Run existing Blimp syntax files

Simulation:

• rblimp_sim - Generate simulated datasets

• SIMULATE - Create simulation specifications

Output & Analysis:

• summary - Model summary

• estimates - Extract parameter estimates

• describe - Descriptive statistics

• psr - Potential Scale Reduction values

• output - Raw Blimp output

• standardized - Extract standardized parameters only

• compare - Compare models

Visualization:

• trace_plot - MCMC trace plots

• posterior_plot - Posterior density plots

• residual_plot - Residual diagnostics

• jn_plot - Johnson-Neyman plots

• simple_plot - Simple slopes plots

• model_table - Publication tables

Utilities:

• by_group - Grouped analysis

• as.mitml - Convert to mitml format

• names - Obtain imputation variable names

• with - Evaluate across imputations

• write.blimp - Write results to files

Setup & Configuration:

• detect_blimp - Detect Blimp installation

• set_blimp - Set path to Blimp executable

• has_blimp - Check if Blimp is available

• update_blimp - Update Blimp installation

Additional Resources

• GitHub repository: https://github.com/blimp-stats/rblimp

• Example files: https://github.com/blimp-stats/rblimp-examples

• Blimp User Guide: https://docs.google.com/document/d/1D3MS79CakuX9mVVvGH13B5nRd9XLttp69oGsvrIRK64

• Report issues: https://github.com/blimp-stats/rblimp/issues

Simulate data using Blimp

Description

Generate simulated datasets using Blimp's SIMULATE command. Supports both single-level and multilevel data generation.

Usage

rblimp_sim(
  model,
  n,
  seed,
  define = NULL,
  variables = NULL,
  tmpfolder,
  nopowershell = FALSE
)

Arguments

Value

A data.frame with simulated data. The returned object has two attributes:

syntax

The Blimp syntax used for simulation (blimp_syntax object)

output

The raw Blimp output (blimp_out object)

Access these with attr(result, "syntax") and attr(result, "output").

See Also

SIMULATE() for creating a simulation specification to use with rblimp()

Examples

# Simple regression: y = 10 + 0.5*x + error
dat <- rblimp_sim(
  model = c(
    "x = normal(0, 1)",
    "y = normal(10 + x*0.5, 1)"
  ),
  n = 1000,
  seed = 10972
)

# Same thing with full quoted syntax
dat <- rblimp_sim(
  model = "x = normal(0, 1);
           y = normal(10 + x*0.5, 1);",
  n = 1000,
  seed = 10972
)

# With parameter definitions
dat <- rblimp_sim(
  model = c(
    "x = normal(0, 1)",
    "y = normal(b0 + b1*x, s2e)"
  ),
  n = 1000,
  define = c("b0 = 10", "b1 = 0.5", "s2e = 1"),
  seed = 10972
)

# Multilevel model
dat <- rblimp_sim(
  model = list(
    schools = c(
      "z = normal(0, 1)",
      "u0 = normal(10 + z*-0.5, 1.0)"
    ),
    students = c(
      "x = normal(0, 1)",
      "y = normal(u0 + x*0.5, 1)"
    )
  ),
  n = list(students = 1000, schools = 100),
  seed = 198723
)

# Access syntax and output
syntax <- attr(dat, "syntax")
print(syntax)

output <- attr(dat, "output")
print(output)

Runs an 'imp' file with Blimp

Description

This function runs 'imp' file with Blimp and captures the output only.

Usage

rblimp_source(file, plots = FALSE, output = TRUE, nopowershell = FALSE)

Arguments

Details

Running rblimp_source will also run a check to see if Blimp is up to date. If Blimp is not up to date, it will prompt the user if it would like to update or not. This check will only be performed on the first run in a session and then every ten hours. This behavior can be disabled by setting the check_blimp_update option to FALSE using options. This check is not performed if R is not being run with an interactive session. See interactive for more information.

Value

a blimp_out object

Examples

# Run blimp script
## Not run: output <- rblimp_source("filepath/to/syntax")

Residuals scores from blimp_obj

Description

Residuals scores from blimp_obj

Usage

## S4 method for signature 'blimp_obj'
resid(object, ...)

Arguments

Value

A list of data frames, one per imputation, each containing the residual columns from the model.

Function to generate conditional regression equation plots (i.e., simple effects) with rblimp and SIMPLE command

Description

Generates a conditional effect plots based on the posterior summaries from the output of rblimp.

Usage

residual_plot(
  model,
  variable,
  nsigma = 1,
  point_col = "black",
  horz_line = "black",
  col1 = "#0571b0",
  col2 = "#ca0020",
  linewidth = 1.1,
  ...
)

Arguments

Details

All colors are passed into ggplot2. See ggplot2::aes_colour_fill_alpha for details on changing colors.

Value

a ggplot2::ggplot plot

Examples

# Generate Data
mydata <- rblimp_sim(
    c(
        'x ~ normal(0, 1)',
        'm ~ normal(0, 1)',
        'y ~ normal(10 + 0.5*x + m + 0.2*x*m, 1)'
    ),
    n = 100,
    seed = 981273
)

# Run Rblimp
m1 <- rblimp(
    'y ~ x m',
    mydata,
    nimps = 10,
    seed = 10972,
    burn = 1000,
    iter = 1000
)

# Generate Plot
residual_plot(m1, 'y') + ggplot2::theme_minimal()

Residuals scores from blimp_obj

Description

Residuals scores from blimp_obj

Usage

## S4 method for signature 'blimp_obj'
residuals(object, ...)

Arguments

Value

A list of data frames, one per imputation, each containing the residual columns from the model.

Set Blimp Executable Location

Description

This function can set the Blimp executable location if it cannot be autodetected.

Usage

set_blimp(exec, beta = FALSE)

Arguments

Details

By default rblimp tries to determine the location of Blimp's computational engine based on standard operating system installation locations. This function is useful for non standard installations or when wanting to use a beta version of blimp's computational engine (which must be installed via the updater).

Value

TRUE if the executable is successfully set; otherwise, it produces an error.

Examples

# example code
# Use blimp executable at `filepath` location
## Not run: set_blimp('filepath')

# Use default blimp location but beta build
set_blimp(beta = TRUE)

Show method for blimp_cp

Description

Show method for blimp_cp

Usage

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

Arguments

Value

No return value, called for the side effect of printing the comparison table to the console.

Show method for blimp_obj

Description

Show method for blimp_obj

Usage

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

Arguments

Value

No return value, called for the side effect of printing the model estimates to the console.

Function to generate conditional regression equation plots (i.e., simple effects) with rblimp and SIMPLE command

Description

Generates a conditional effect plots based on the posterior summaries from the output of rblimp.

Usage

simple_plot(formula, model, ci = 0.95, xvals, ...)

Arguments

Details

To change colors use ggplot2's scale system. Both fill and color are used. See ggplot2::aes_colour_fill_alpha for more information about setting a manual set of colors.

For nominal moderators, the variable must include the nominal code used in the dummy codes (e.g., moderator.1, moderator.2, etc). When there are multiple dummy codes, then all codes must be listed using a +. For example, after the ~ the following statement can be included:

\code{focal | moderator.1 + moderator.2}

Value

a ggplot2::ggplot plot

Examples

# Generate Data
mydata <- rblimp_sim(
    c(
        'x ~ normal(0, 1)',
        'm ~ normal(0, 1)',
        'y ~ normal(10 + 0.5*x + m + 0.2*x*m, 1)'
    ),
    n = 100,
    seed = 981273
)

# Run Rblimp
m1 <- rblimp(
    'y ~ x m x*m',
    mydata,
    center = ~ m,
    simple = 'x | m',
    seed = 10972,
    burn = 1000,
    iter = 1000
)

# Generate Plot
simple_plot(y ~ x | m, m1)

Extract standardized solutions from Blimp

Description

Extracts the data information section from Blimp output.

Usage

standardized(object)

Arguments

Value

A base::matrix with standardized solutions

Summary method for blimp_obj

Description

Provides formatted summary output for blimp_obj, optionally allowing selection by variable name or block. Offers cleaner presentation than the raw estimates matrix.

Usage

## S4 method for signature 'blimp_obj'
summary(object, selector, digits = 3, ...)

Arguments

Value

If selector is provided: invisibly returns a matrix or list of estimates for the selected variable/block. If no selector: returns the full estimates matrix with improved formatting.

Function to generate trace plots of the burn-in iterations

Description

Generates ggplot2::ggplot plots using ggplot2::geom_line based on the output from rblimp

Usage

trace_plot(model, selector, ...)

Arguments

Details

To change colors use ggplot2's scale system. Both fill and color are used. See ggplot2::aes_colour_fill_alpha for more information about setting a manual set of colors.

Value

a ggplot2::ggplot plot

Examples

# Generate Data
mydata <- rblimp_sim(
    c(
        'x ~ normal(0, 1)',
        'm ~ normal(0, 1)',
        'y ~ normal(10 + 0.5*x + m + 0.2*x*m, 1)'
    ),
    n = 100,
    seed = 981273
)

# Run Rblimp
m1 <- rblimp(
    c(
        'y ~ x m',
        'x ~~ m'
    ),
    mydata,
    seed = 10972,
    burn = 1000,
    iter = 1000
)

# Generate plot of all parameters with `y`
trace_plot(m1, 'y') + ggplot2::theme_minimal()

# Generate plot of all parameters with `y`
# Add limits to only graph first 250 iterations
trace_plot(m1, 'y') + ggplot2::xlim(0, 250) + ggplot2::theme_minimal()

# Generate plot of all parameters
trace_plot(m1) + ggplot2::theme_minimal()

# Generate plot of all parameters for `y` and `x`
trace_plot(m1, c('x', 'y')) + ggplot2::theme_minimal()
# Generate Plot of Parameter 5
trace_plot(m1, 5) + ggplot2::theme_minimal()

# Generate plot of `x residual variance`
trace_plot(m1, 'x residual variance') + ggplot2::theme_minimal()

# Generate plot of Parameters 7 and 9
trace_plot(m1, c(7, 9)) + ggplot2::theme_minimal()

Runs the Blimp Updater in order to Update Blimp

Description

Runs the Blimp Updater in order to Update Blimp

Usage

update_blimp()

Details

If the session is not an interactive session then it will not open the updater.

Value

Logical if the updater was opened or not

Examples

# Open Blimp Updater
update_blimp()

Fit Model across imputations with mitml package using by_group

Description

Fit Model across imputations with mitml package using by_group

Usage

## S4 method for signature 'blimp_bygroup'
with(data, expr, ...)

Arguments

Value

A list of class "mitml.result" containing the results of evaluating expr on each imputed data set.

Fit Model across imputations with mitml package

Description

Fit Model across imputations with mitml package

Usage

## S4 method for signature 'blimp_obj'
with(data, expr, ...)

Arguments

Value

A list of class "mitml.result" containing the results of evaluating expr on each imputed data set.

A function to write out blimp input and output from a model

Description

A function to write out blimp input and output from a model

Usage

write.blimp(object, folder = "")

## S4 method for signature 'blimp_syntax'
write.blimp(object, folder = "")

## S4 method for signature 'blimp_out'
write.blimp(object, folder = "")

## S4 method for signature 'blimp_obj'
write.blimp(object, folder = "")

Arguments

Value

No return value, called for its side effect of writing 'Blimp' input and output files to disk.

Methods (by class)

• write.blimp(blimp_syntax): Write blimp_syntax to file

• write.blimp(blimp_out): Write blimp_out to file

• write.blimp(blimp_obj): Write blimp_obj files to folder

Examples

# Generate Data with `rblimp_sim`
mydata <- rblimp_sim(
    c(
        'f ~ normal(0, 1)',
        'x1:x5 ~ normal(f, 1)',
        'y ~ normal(10 + 0.3*f, 1 - .3^2)'
     ),
     n = 500,
     seed = 19723,
     variables = c('y', 'x1:x5')
)

# Fit SEM Model
model <- rblimp(
    list(
        structure = 'y ~ f',
        measurement = 'f -> x1:x5'
    ),
    mydata,
    seed = 3927,
    latent = ~ f
)

# Write out input and output
## Not run: 
write.blimp(model, "folder_location")

## End(Not run)