Either a character string (typically between single quotes) or
a parameter table containing additional (currently fixed-to-zero) parameters
for which the score test must be computed.
release
Vector of Integers. The indices of the constraints that
should be released. The indices correspond to the order of the equality
constraints as they appear in the parameter table.
univariate
Logical. If TRUE, compute the univariate score
statistics, one for each constraints.
cumulative
Logical. If TRUE, order the univariate score
statistics from large to small, and compute a series of
multivariate score statistics, each time adding an additional constraint.
epc
Logical. If TRUE, and we are releasing existing constraints,
compute the expected parameter changes for the existing (free) parameters,
for each released constraint.
verbose
Logical. Not used for now.
warn
Logical. If TRUE, print out warnings if they occur.
Details
This function can be used to compute both multivariate and univariate
score tests. There are two modes: 1) releasing fixed-to-zero parameters
(using the add argument), and 2) releasing existing equality
constraints (using the release argument). The two modes can not
be used simultaneously.
When adding new parameters, they should not already be part of the model
(i.e. not listed in the parameter table). If you want to test for
a parameter that was explicitly fixed to a constant (say to zero),
it is better to label the parameter, and use an explicit equality
constraint.
Value
A list containing at least three elements: the Score test statistic
(stat), the degrees of freedom (df), and a p-value under the
chi-square distribution (p.value). If univariate tests were requested,
an additional element (TS.univariate) containing a numeric vector of
univariate score statistics. If cumulative tests were requested,
an additional element (TS.order) showing the order of the univariate
test statistics, and an element (TS.cumulative) containing a numeric
vector of cumulative multivariate score statistics.
References
Bentler, P. M., & Chou, C. P. (1993). Some new covariance structure model
improvement statistics. Sage Focus Editions, 154, 235-255.
Examples
HS.model <- '
visual =~ x1 + b1*x2 + x3
textual =~ x4 + b2*x5 + x6
speed =~ x7 + b3*x8 + x9
b1 == b2
b2 == b3
'
fit <- cfa(HS.model, data=HolzingerSwineford1939)
# test 1: release both two equality constraints
lavTestScore(fit, cumulative = TRUE)
# test 2: the score test for adding two (currently fixed
# to zero) cross-loadings
newpar = '
visual =~ x9
textual =~ x3
'
lavTestScore(fit, add = newpar)