View source: R/runMImodification.R
modindices.mi  R Documentation 
Modification indices (1df Lagrange multiplier tests) from a latent variable model 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 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 (1991)—or by pooling the completedata scoretest statistics across imputed data sets (i.e., "D2"; Li et al., 1991).
modindices.mi(object, test = c("D2", "D1"), omit.imps = c("no.conv", "no.se"), standardized = TRUE, cov.std = TRUE, information = "expected", power = FALSE, delta = 0.1, alpha = 0.05, high.power = 0.75, sort. = FALSE, minimum.value = 0, maximum.number = nrow(LIST), na.remove = TRUE, op = NULL) modificationIndices.mi(object, test = c("D2", "D1"), omit.imps = c("no.conv", "no.se"), standardized = TRUE, cov.std = TRUE, information = "expected", power = FALSE, delta = 0.1, alpha = 0.05, high.power = 0.75, sort. = FALSE, minimum.value = 0, maximum.number = nrow(LIST), na.remove = TRUE, op = NULL)
object 
An object of class 
test 

omit.imps 

standardized 

cov.std 

information 

power 

delta 
The value of the effect size, as used in the posthoc power
computation, currently using the unstandardized metric of the 
alpha 
The significance level used for deciding if the modification index is statistically significant or not. 
high.power 
If the computed power is higher than this cutoff value,
the power is considered 'high'. If not, the power is considered 'low'.
This affects the values in the 
sort. 

minimum.value 

maximum.number 

na.remove 

op 

A data.frame
containing modification indices and (S)EPCs.
When test = "D2"
, each (S)EPC will be pooled by taking its
average across imputations. When test = "D1"
, EPCs will be
calculated in the standard way using the pooled gradient and information,
and SEPCs will be calculated by standardizing the EPCs using modelimplied
(residual) variances.
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)
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
## 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 < ' visual =~ x1 + x2 + x3 textual =~ x4 + x5 + x6 speed =~ x7 + x8 + x9 ' out < cfa.mi(HS.model, data = imps) modindices.mi(out) # default: Li et al.'s (1991) "D2" method modindices.mi(out, test = "D1") # Li et al.'s (1991) "D1" method ## End(Not run)
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