Given a fitted lavaan object, compute the modification indices (= univariate score tests) for a selected set of fixed-to-zero 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)
```

A data.frame containing modification indices and EPC's.

- object
An object of class

`lavaan`

.- standardized
If

`TRUE`

, two extra columns (sepc.lv and sepc.all) will contain standardized values for the EPCs. In the first column (sepc.lv), standardization is based on the variances of the (continuous) latent variables. In the second column (sepc.all), standardization is based on both the variances of both (continuous) observed and latent variables. (Residual) covariances are standardized using (residual) variances.- cov.std
Logical. See

`standardizedSolution`

.- information
`character`

indicating the type of information matrix to use (check`lavInspect`

for available options).`"expected"`

information is the default, which provides better control of Type I errors.- power
If

`TRUE`

, the (post-hoc) power is computed for each modification index, using the values of`delta`

and`alpha`

.- delta
The value of the effect size, as used in the post-hoc 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

`sort.`

option.- 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

`op`

.

Modification indices are just 1-df (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 chi-square
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 fixed-to-zero (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
fixed-to-zero 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.

```
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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