betaMC (version 1.3.3)

MCMI: Generate the Sampling Distribution of Regression Parameters Using the Monte Carlo Method for Data with Missing Values

Description

Generate the Sampling Distribution of Regression Parameters Using the Monte Carlo Method for Data with Missing Values

Usage

MCMI(
  object,
  mi,
  R = 20000L,
  type = "hc3",
  g1 = 1,
  g2 = 1.5,
  k = 0.7,
  decomposition = "eigen",
  pd = TRUE,
  tol = 1e-06,
  fixed_x = FALSE,
  seed = NULL
)

Value

Returns an object of class mc which is a list with the following elements:

call

Function call.

args

Function arguments.

lm_process

Processed lm object.

scale

Sampling variance-covariance matrix of parameter estimates.

location

Parameter estimates.

thetahatstar

Sampling distribution of parameter estimates.

fun

Function used ("MCMI").

Arguments

object

Object of class lm.

mi

Object of class mids (output of mice::mice()), object of class amelia (output of Amelia::amelia()), or a list of multiply imputed data sets.

R

Positive integer. Number of Monte Carlo replications.

type

Character string. Sampling covariance matrix type. Possible values are "mvn", "adf", "hc0", "hc1", "hc2", "hc3", "hc4", "hc4m", and "hc5". type = "mvn" uses the normal-theory sampling covariance matrix. type = "adf" uses the asymptotic distribution-free sampling covariance matrix. type = "hc0" through "hc5" uses different versions of heteroskedasticity-consistent sampling covariance matrix.

g1

Numeric. g1 value for type = "hc4m".

g2

Numeric. g2 value for type = "hc4m".

k

Numeric. Constant for type = "hc5"

decomposition

Character string. Matrix decomposition of the sampling variance-covariance matrix for the data generation. If decomposition = "chol", use Cholesky decomposition. If decomposition = "eigen", use eigenvalue decomposition. If decomposition = "svd", use singular value decomposition.

pd

Logical. If pd = TRUE, check if the sampling variance-covariance matrix is positive definite using tol.

tol

Numeric. Tolerance used for pd.

fixed_x

Logical. If fixed_x = TRUE, treat the regressors as fixed. If fixed_x = FALSE, treat the regressors as random.

seed

Integer. Seed number for reproducibility.

Author

Ivan Jacob Agaloos Pesigan

Details

Multiple imputation is used to deal with missing values in a data set. The vector of parameter estimates and the corresponding sampling covariance matrix are estimated for each of the imputed data sets. Results are combined to arrive at the pooled vector of parameter estimates and the corresponding sampling covariance matrix. The pooled estimates are then used to generate the sampling distribution of regression parameters. See MC() for more details on the Monte Carlo method.

References

Dudgeon, P. (2017). Some improvements in confidence intervals for standardized regression coefficients. Psychometrika, 82(4), 928–951. tools:::Rd_expr_doi("10.1007/s11336-017-9563-z")

MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39(1), 99-128. tools:::Rd_expr_doi("10.1207/s15327906mbr3901_4")

Pesigan, I. J. A., & Cheung, S. F. (2024). Monte Carlo confidence intervals for the indirect effect with missing data. Behavior Research Methods. tools:::Rd_expr_doi("10.3758/s13428-023-02114-4")

Preacher, K. J., & Selig, J. P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6(2), 77–98. tools:::Rd_expr_doi("10.1080/19312458.2012.679848")

See Also

Other Beta Monte Carlo Functions: BetaMC(), DeltaRSqMC(), DiffBetaMC(), MC(), PCorMC(), RSqMC(), SCorMC()

Examples

# Data ---------------------------------------------------------------------
data("nas1982", package = "betaMC")
nas1982_missing <- mice::ampute(nas1982)$amp # data set with missing values

# Multiple Imputation
mi <- mice::mice(nas1982_missing, m = 5, seed = 42, print = FALSE)

# Fit Model in lm ----------------------------------------------------------
## Note that this does not deal with missing values.
## The fitted model (`object`) is updated with each imputed data
## within the `MCMI()` function.
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982_missing)

# Monte Carlo --------------------------------------------------------------
mc <- MCMI(
  object,
  mi = mi,
  R = 100, # use a large value e.g., 20000L for actual research
  seed = 0508
)
mc
# The `mc` object can be passed as the first argument
# to the following functions
#   - BetaMC
#   - DeltaRSqMC
#   - DiffBetaMC
#   - PCorMC
#   - RSqMC
#   - SCorMC