calculate.D2  R Documentation 
This is a utility function used to calculate the "D2" statistic for pooling
test statistics across multiple imputations. This function is called by
several functions used for lavaan.mi
objects, such as
lavTestLRT.mi
, lavTestWald.mi
, and
lavTestScore.mi
. But this function can be used for any general
scenario because it only requires a vector of χ^2 statistics (one
from each imputation) and the degrees of freedom for the test statistic.
See Li, Meng, Raghunathan, & Rubin (1991) and Enders (2010, chapter 8) for
details about how it is calculated.
calculate.D2(w, DF = 0L, asymptotic = FALSE)
w 

DF 
degrees of freedom (df) of the χ^2 statistics.
If 
asymptotic 

A numeric
vector containing the test statistic, df,
its p value, and 2 missingdata diagnostics: the relative invrease
in variance (RIV, or average for multiparameter tests: ARIV) and the
fraction missing information (FMI = ARIV / (1 + ARIV)).
Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)
Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford.
Li, K.H., Meng, X.L., Raghunathan, T. E., & Rubin, D. B. (1991). Significance levels from repeated pvalues with multiplyimputed data. Statistica Sinica, 1(1), 65–92. Retrieved from https://www.jstor.org/stable/24303994
lavTestLRT.mi
, lavTestWald.mi
,
lavTestScore.mi
## generate a vector of chisquared values, just for example DF < 3 # degrees of freedom M < 20 # number of imputations CHI < rchisq(M, DF) ## pool the "results" calculate.D2(CHI, DF) # by default, an F statistic is returned calculate.D2(CHI, DF, asymptotic = TRUE) # asymptotically chisquared ## generate standardnormal values, for an example of Wald z tests Z < rnorm(M) calculate.D2(Z) # default DF = 0 will square Z to make chisq(DF = 1) ## F test is equivalent to a t test with the denominator DF
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