modificationIndices  R Documentation 
Given a fitted lavaan object, compute the modification indices (= univariate score tests) for a selected set of fixedtozero parameters.
modificationIndices(object, 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), free.remove = TRUE,
na.remove = TRUE, op = NULL)
modindices(object, 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), free.remove = TRUE,
na.remove = TRUE, op = NULL)
object 
An object of class 
standardized 
If 
cov.std 
Logical. See 
information 

power 
If 
delta 
The value of the effect size, as used in the posthoc power computation, currently using the unstandardized metric of the epc column. 
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 'decision' column in the output. 
sort. 
Logical. If TRUE, sort the output using the values of the modification index values. Higher values appear first. 
minimum.value 
Numeric. Filter output and only show rows with a modification index value equal or higher than this minimum value. 
maximum.number 
Integer. Filter output and only show the first
maximum number rows. Most useful when combined with the 
free.remove 
Logical. If TRUE, filter output by removing all rows corresponding to free (unconstrained) parameters in the original model. 
na.remove 
Logical. If TRUE, filter output by removing all rows with NA values for the modification indices. 
op 
Character string. Filter the output by selecting only those rows with
operator 
Modification indices are just 1df (or univariate) score tests. The
modification index (or score test) for a single parameter reflects
(approximately) the improvement in model fit (in terms of the chisquare
test statistic), if we would refit the model but allow this parameter to
be free.
This function is a convenience function in the sense that it produces a
(hopefully sensible) table of currently fixedtozero (or fixed to another
constant) parameters. For each of these parameters, a modification index
is computed, together with an expected parameter change (epc) value.
It is important to realize that this function will only consider
fixedtozero parameters. If you have equality constraints in the model,
and you wish to examine what happens if you release all (or some) of these
equality constraints, use the lavTestScore
function.
A data.frame containing modification indices and EPC's.
HS.model < ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit < cfa(HS.model, data=HolzingerSwineford1939)
modindices(fit, minimum.value = 10, sort = TRUE)
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