Modification indices of a latent variable model.
modificationIndices(object, standardized = TRUE, cov.std = TRUE,
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, 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)
If TRUE
, two extra columns (sepc.lv and sepc.all)
will contain standardized values for the epc's. In the first column (sepc.lv),
standardizization 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.
Logical. See standardizedSolution
.
If TRUE
, the (post-hoc) power is computed for each
modification index, using the values of delta
and alpha
.
The value of the effect size, as used in the post-hoc power computation, currently using the unstandardized metric of the epc column.
The significance level used for deciding if the modification index is statistically significant or not.
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.
Logical. If TRUE, sort the output using the values of the modification index values. Higher values appear first.
Numeric. Filter output and only show rows with a modification index value equal or higher than this minimum value.
Integer. Filter output and only show the first
maximum number rows. Most useful when combined with the sort.
option.
Logical. If TRUE, filter output by removing all rows corresponding to free (unconstrained) parameters in the original model.
Logical. If TRUE, filter output by removing all rows with NA values for the modification indices.
Character string. Filter the output by selectin only those rows with
operator op
.
A data.frame containing modification indices and EPC's.
# NOT RUN {
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
fit <- cfa(HS.model, data=HolzingerSwineford1939)
modindices(fit)
# }
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