lavaan: modificationIndices – R documentation

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modificationIndices

Modification Indices

Description

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

Usage

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)

Arguments

`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`.

Details

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 (of 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.

Value

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

Examples

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)

lavaan

Latent Variable Analysis

v0.6-10

GPL (>= 2)

Authors

Yves Rosseel [aut, cre] (<https://orcid.org/0000-0002-4129-4477>), Terrence D. Jorgensen [aut] (<https://orcid.org/0000-0001-5111-6773>), Nicholas Rockwood [aut] (<https://orcid.org/0000-0001-5931-183X>), Daniel Oberski [ctb], Jarrett Byrnes [ctb], Leonard Vanbrabant [ctb], Victoria Savalei [ctb], Ed Merkle [ctb], Michael Hallquist [ctb], Mijke Rhemtulla [ctb], Myrsini Katsikatsou [ctb], Mariska Barendse [ctb], Florian Scharf [ctb], Han Du [ctb]

Initial release