Nonparametric Bootstrap Confidence Intervals for the Indirect Effect using Multiple Imputation (MI nested within NB or NB(MI))

Nonparametric Bootstrap Confidence Intervals for the Indirect Effect using Multiple Imputation (MI nested within NB or NB(MI))

Usage

NBMI(
  data_missing,
  data_mi,
  B = 5000L,
  m = 100L,
  alpha = c(0.05, 0.01, 0.001),
  mplus_bin
)

Arguments

data_missing

Numeric matrix. Output of the AmputeData function or a three-column data set with missing data.

data_mi

List of numeric matrices. Output of the ImputeData function or a list of three-column data sets with imputed data.

B

Positive integer. Number of bootstrap samples.

m

Positive integer. Number of imputations.

alpha

Numeric vector. Significance level.

mplus_bin

Character string. Path of Mplus binary.

Value

Nonparametric bootstrap confidence intervals. bc corresponds to bias-corrected and pc corresponds to percentile confidence intervals.

References

Zhang, Z., & Wang, L. (2012). Methods for mediation analysis with missing data. Psychometrika, 78(1), 154–184. doi:10.1007/s11336-012-9301-5

Zhang, Z., Wang, L., & Tong, X. (2015). Mediation analysis with missing data through multiple imputation and bootstrap. Quantitative psychology research (pp. 341–355). Springer International Publishing. doi:10.1007/978-3-319-19977-1_24

See also

Other Confidence Interval Functions: MCMI(), MCML(), MINB(), NBML()

Author

Ivan Jacob Agaloos Pesigan