Description Usage Arguments Value Author(s) References See Also Examples
Score test (or "Lagrange multiplier" test) for lavaan models fitted to multiple imputed data sets. Statistics for releasing one or more fixed or constrained parameters in model can be calculated by pooling the gradient and information matrices pooled across imputed data sets in a method proposed by Mansolf, Jorgensen, & Enders (2020)—analogous to the "D1" Wald test proposed by Li, Meng, Raghunathan, & Rubin's (1991)—or by pooling the completedata scoretest statistics across imputed data sets (i.e., "D2"; Li et al., 1991).
1 2 3 4 5  lavTestScore.mi(object, add = NULL, release = NULL, test = c("D2", "D1"),
scale.W = !asymptotic, omit.imps = c("no.conv", "no.se"),
asymptotic = is.null(add), univariate = TRUE, cumulative = FALSE,
epc = FALSE, standardized = epc, cov.std = epc, verbose = FALSE,
warn = TRUE, information = "expected")

object 
An object of class 
add 
Either a 
release 
Vector of 
test 

scale.W 

omit.imps 

asymptotic 

univariate 

cumulative 

epc 

standardized 
If 
cov.std 

verbose 

warn 

information 

A list containing at least one data.frame
:
$test
: The total score test, with columns for the score
test statistic (X2
), its degrees of freedom (df
), its
p value under the χ^2 distribution (p.value
),
and if asymptotic=FALSE
, the average relative invrease in
variance (ARIV) used to calculate the denominator df is also
returned as a missingdata diagnostic, along with the fraction missing
information (FMI = ARIV / (1 + ARIV)).
$uni
: Optional (if univariate=TRUE
).
Each 1df score test, equivalent to modification indices. Also
includes EPCs if epc=TRUE
, and RIV and FMI if
asymptotic=FALSE
.
$cumulative
: Optional (if cumulative=TRUE
).
Cumulative score tests, with ARIV and FMI if asymptotic=FALSE
.
$epc
: Optional (if epc=TRUE
). Parameter estimates,
expected parameter changes, and expected parameter values if ALL
the tested constraints were freed.
See lavTestScore
for details.
Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)
Adapted from lavaan source code, written by Yves Rosseel (Ghent University; Yves.Rosseel@UGent.be)
test = "D1"
method proposed by
Maxwell Mansolf (University of California, Los Angeles;
mamansolf@gmail.com)
Bentler, P. M., & Chou, C.P. (1992). Some new covariance structure model improvement statistics. Sociological Methods & Research, 21(2), 259–282. doi: 10.1177/0049124192021002006
Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford.
Li, K.H., Meng, X.L., Raghunathan, T. E., & Rubin, D. B. (1991). Significance levels from repeated pvalues with multiplyimputed data. Statistica Sinica, 1(1), 65–92. Retrieved from https://www.jstor.org/stable/24303994
Mansolf, M., Jorgensen, T. D., & Enders, C. K. (2020). A multiple imputation score test for model modification in structural equation models. Psychological Methods, 25(4), 393–411. doi: 10.1037/met0000243
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  ## Not run:
## impose missing data for example
HSMiss < HolzingerSwineford1939[ , c(paste("x", 1:9, sep = ""),
"ageyr","agemo","school")]
set.seed(12345)
HSMiss$x5 < ifelse(HSMiss$x5 <= quantile(HSMiss$x5, .3), NA, HSMiss$x5)
age < HSMiss$ageyr + HSMiss$agemo/12
HSMiss$x9 < ifelse(age <= quantile(age, .3), NA, HSMiss$x9)
## impute missing data
library(Amelia)
set.seed(12345)
HS.amelia < amelia(HSMiss, m = 20, noms = "school", p2s = FALSE)
imps < HS.amelia$imputations
## specify CFA model from lavaan's ?cfa help page
HS.model < '
speed =~ c(L1, L1)*x7 + c(L1, L1)*x8 + c(L1, L1)*x9
'
out < cfa.mi(HS.model, data = imps, group = "school", std.lv = TRUE)
## Mode 1: Score test for releasing equality constraints
## default test: Li et al.'s (1991) "D2" method
lavTestScore.mi(out, cumulative = TRUE)
## Li et al.'s (1991) "D1" method
lavTestScore.mi(out, test = "D1")
## Mode 2: Score test for adding currently fixedtozero parameters
lavTestScore.mi(out, add = 'x7 ~~ x8 + x9')
## End(Not run)

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