Package 'mmiCATs'

CloseCATs Shiny Application

Description

This function creates and runs a Shiny application for the CloseCATs game. The application provides a user interface for setting up the game, dealing cards, swapping cards, and scoring the game based on statistical computations. The game involves dealing cards to players and the computer, allowing the player to swap cards in their column, and scoring the game based on the mispecification distance calculated from the processed hands.

Usage

CloseCATs()

Details

The UI allows players to input various statistical parameters and preferences for the game setup. It also provides interactive elements for dealing cards, swapping cards within a column, and scoring the game based on the calculated mispecification distance.

The main components of the Shiny application include:

• A sidebar for inputting game parameters and controls for dealing and scoring.

• A main panel for displaying game cards, swap options, and results.

• Reactive elements that update based on user interaction and game state.

Value

A Shiny app object which can be run to start the application.

Examples

# To run the CloseCATs Shiny application:
if(interactive()){
  CloseCATs()
}

---

Cluster-Adjusted Confidence Intervals And p-Values Robust GLMs

Description

Performs cluster-adjusted inference on a robust generalized linear model object, using robust generalized linear regression within each cluster. This function is tailored for models where observations are clustered, and standard errors need adjustment for clustering. The function applies a robust generalized linear regression model to each cluster using the specified family and method, and then aggregates the results.

Usage

cluster_im_glmRob(
  robmod,
  dat,
  cluster,
  ci.level = 0.95,
  drop = TRUE,
  return.vcv = FALSE,
  engine = "robust",
  ...
)

Arguments

robmod	A robust generalized linear model object created using robust::glmRob() or robustbase::glmrob(). It must contain elements 'formula', 'family', and 'method'.
dat	A data frame containing the data used in the model.
cluster	A formula indicating the clustering variable in dat.
ci.level	Confidence level for the confidence intervals, default is 0.95.
drop	Logical; if TRUE, drops clusters where the model does not converge.
return.vcv	Logical; if TRUE, the variance-covariance matrix of the cluster-averaged coefficients will be returned.
engine	Set the engine to "robust" to use robust::glmRob() or "robustbase" to use robustbase::glmrob(). Default is "robust".
...	Additional arguments to be passed to robust::glmRob() or robustbase::glmrob().

Value

An invisible list containing the following elements:

p.values

A matrix of p-values for each independent variable.

ci

A matrix with the lower and upper bounds of the confidence intervals for each independent variable.

vcv.hat

The variance-covariance matrix of the cluster-averaged coefficients, returned if return.vcv is TRUE.

beta.bar

The cluster-averaged coefficients, returned if return.vcv is TRUE.

Examples

iris_bin <- iris
# Create a binary variable for MPG (e.g., MPG > 20)
iris_bin$high_Sepal.Length = as.factor(ifelse(iris_bin$Sepal.Length > 5.8, 1, 0))

robout <- robustbase::glmrob(formula = high_Sepal.Length ~ Petal.Length + Petal.Width,
                             family = binomial,
                             data = iris_bin)
cluster_im_glmRob(robout, dat = iris_bin, ~Species, return.vcv = TRUE,
                  engine = "robustbase")

---

Cluster-Adjusted Confidence Intervals And p-Values Robust Linear Models

Description

Performs cluster-adjusted inference on a robust linear model object, using robust linear regression within each cluster. This function is designed to handle models where observations are clustered, and standard errors need to be adjusted to account for this clustering. The function applies a robust linear regression model to each cluster and then aggregates the results.

Usage

cluster_im_lmRob(
  robmod,
  formula,
  dat,
  cluster,
  ci.level = 0.95,
  drop = TRUE,
  return.vcv = FALSE,
  engine = "robust",
  ...
)

Arguments

robmod	A robust linear model object created using robust::lmRob() or robustbase::lmrob().
formula	A formula or a string that can be coerced to a formula.
dat	A data frame containing the data used in the model.
cluster	A formula indicating the clustering variable in dat.
ci.level	Confidence level for the confidence intervals, default is 0.95.
drop	Logical; if TRUE, drops clusters where the model does not converge.
return.vcv	Logical; if TRUE, the variance-covariance matrix of the cluster-averaged coefficients will be returned.
engine	Set the engine to "robust" to use robust::lmRob() or "robustbase" to use robustbase::lmrob(). Default is "robust".
...	Additional arguments to be passed to the robust::lmRob() or the robustbase::lmrob() function.

Value

A list containing the following elements:

p.values

A matrix of p-values for each independent variable.

ci

A matrix with the lower and upper bounds of the confidence intervals for each independent variable.

vcv.hat

The variance-covariance matrix of the cluster-averaged coefficients, returned if return.vcv is TRUE.

beta.bar

The cluster-averaged coefficients, returned if return.vcv is TRUE.

Examples

form <- Sepal.Length ~ Petal.Length + Petal.Width
mod <- robust::lmRob(formula = form, dat = iris)
cluster_im_lmRob(robmod = mod, formula = form, dat = iris,cluster = ~Species)

---

Launch KenRCATs Shiny Application

Description

This function launches a 'shiny' application for conducting power analysis simulations using CATs (Clustered Adjusted t-statistics) and Kenward-Roger methods. The app allows users to input simulation parameters, run simulations, view results, and manage data in a PostgreSQL database.

Usage

KenRCATs(dbname, datatable, host, port, user, password)

Arguments

dbname	Character string specifying the name of the PostgreSQL database.
datatable	Character string specifying the name of the table in the database.
host	Character string specifying the host name or IP address of the database server.
port	Integer specifying the port number on which the database is running.
user	Character string specifying the username for database connection.
password	Character string specifying the password for database connection.

Details

The KenRCATs function sets up a Shiny application with the following features:

• Input fields for various simulation parameters

• Ability to run power analysis simulations

• Display of simulation results

• Option to submit results to a PostgreSQL database

• Functionality to download data from the database

• Display of relevant citations

Value

A 'shiny' app object.

Examples

if(interactive()){
  KenRCATs(
    dbname = "your_database_name",
    datatable = "your_table_name",
    host = "localhost",
    port = 5432,
    user = "your_username",
    password = "your_password"
  )
}

---

Kenward-Roger Analysis Shiny Application

Description

A Shiny application that allows users to upload a dataset, modify variable types, and fit a mixed-effects model using the Kenward-Roger approximation for small sample inference.

Usage

kenward_roger()

Details

The application provides an interactive interface for setting up and running a mixed-effects model analysis with the Kenward-Roger method for estimating the degrees of freedom in linear mixed-effects models.

Value

A Shiny application object.

References

Kenward, M. G., & Roger, J. H. (1997). Small Sample Inference for Fixed Effects from Restricted Maximum Likelihood. Biometrics, 53, 983-997.

Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest Package: Tests in Linear Mixed Effects Models. Journal of Statistical Software, 82(13), 1-26. <doi: 10.18637/jss.v082.i13>.

Examples

if (interactive()) {
  kenward_roger()
}

---

Set Up CATs Analysis in Shiny Application

Description

This function creates a Shiny application for performing CATs (Cluster-Adjusted t-statistics) analysis. It provides a user interface for uploading a CSV file, specifying the model and additional arguments, and running the analysis. The output includes variable selection, GLM (Generalized Linear Model) summary, and results of the CATs analysis.

Usage

mmiCATs()

Details

The application allows the user to upload a dataset, specify a GLM model and additional arguments, and run CATs analysis. The UI consists of various input elements like file upload, text input, numeric input, and action buttons. The server part handles the data processing, model fitting, and execution of the CATs analysis. The application outputs include the list of variables, GLM model summary, and the results from the CATs analysis.

Value

A Shiny app object which can be run to start the application.

References

Esarey J, Menger A. Practical and Effective Approaches to Dealing With Clustered Data. Political Science Research and Methods. 2019;7(3):541-559. doi:10.1017/psrm.2017.42

Examples

# To run the Shiny app
if(interactive()){
  mmiCATs()
}

---

Power Analysis for Clustered Data

Description

Conducts a power analysis for clustered data using simulation. This function allows for comparing the performance of different estimation methods in terms of power, rejection rate, root mean square error (RMSE), relative RMSE, coverage probability, and average confidence interval width.

Usage

pwr_func_lmer(
  betas = list(int = 0, x1 = -5, x2 = 2, x3 = 10),
  dists = list(x1 = stats::rnorm, x2 = stats::rbinom, x3 = stats::rnorm),
  distpar = list(x1 = list(mean = 0, sd = 1), x2 = list(size = 1, prob = 0.4), x3 =
    list(mean = 1, sd = 2)),
  N = 25,
  reps = 1000,
  alpha = 0.05,
  var_intr = "x1",
  grp = "ID",
  mod = paste0("out ~ x1 + x2 + x3 + (x3|", grp, ")"),
  catsmod = "out ~ x1 + x2 + x3",
  r_slope = "x1",
  r_int = "int",
  n_time = 20,
  mean_i = 0,
  var_i = 1,
  mean_s = 0,
  var_s = 1,
  cov_is = 0,
  mean_r = 0,
  var_r = 1,
  cor_mat = NULL,
  corvars = NULL,
  time_index = NULL
)

Arguments

betas	Named list of true coefficient values for the fixed effects.
dists	Named list of functions to generate random distributions for each predictor.
distpar	Named list of parameter lists for each distribution function in dists.
N	Integer specifying the number of groups.
reps	Integer specifying the number of replications for the simulation.
alpha	Numeric value specifying the significance level for hypothesis testing.
var_intr	Character string specifying the name of the variable of interest (for power calculations).
grp	Character string specifying the name of the grouping variable.
mod	Formula for fitting mixed-effects model to simulated data.
catsmod	Formula for the CATs model.
r_slope	Character string specifying the name of the random slope variable. This is the random slope when simulating data.
r_int	Character string specifying the name of the random intercept.
n_time	Integer or vector specifying the number of time points per group. If a vector, its length must equal N.
mean_i	Numeric value specifying the mean for the random intercept.
var_i	Numeric value specifying the variance for the random intercept.
mean_s	Numeric value specifying the mean for the random slope.
var_s	Numeric value specifying the variance for the random slope.
cov_is	Numeric value specifying the covariance between the random intercept and slope.
mean_r	Numeric value specifying the mean for the residual error.
var_r	Numeric value specifying the variance for the residual error.
cor_mat	Correlation matrix for correlated predictors, if any.
corvars	List of vectors, each vector containing names of correlated variables.
time_index	Either "linear" for a linear time trend, a custom function, or a character string that evaluates to a function for generating time values. If specified, 'time' should be included in betas but not in dists.

Value

A dataframe summarizing the results of the power analysis, including average coefficient estimate, rejection rate, root mean square error, relative root mean square error, coverage probability, and average confidence interval width for each method.

Examples

# Basic usage with default parameters
pwr_func_lmer(reps = 2)

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