Description Usage Arguments Details Value References Examples
View source: R/lav_test_score.R
Score test (or Lagrange Multiplier test) for releasing one or more fixed or constrained parameters in model.
1 2 3 
object 
An object of class 
add 
Either a character string (typically between single quotes) or a parameter table containing additional (currently fixedtozero) 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 
cumulative 
Logical. If 
epc 
Logical. If 
verbose 
Logical. Not used for now. 
warn 
Logical. If 
information 

This function can be used to compute both multivariate and univariate
score tests. There are two modes: 1) releasing fixedtozero 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.
A list containing at least one data.frame
:
$test
: The total score test, with columns for the score
test statistic (X2
), the degrees of freedom (df
), and
a p value under the χ^2 distribution (p.value
).
$uni
: Optional (if univariate=TRUE
).
Each 1df score test, equivalent to modification indices.
$cumulative
: Optional (if cumulative=TRUE
).
Cumulative score tests.
$epc
: Optional (if epc=TRUE
). Parameter estimates,
expected parameter changes, and expected parameter values if all
the tested constraints were freed.
Bentler, P. M., & Chou, C. P. (1993). Some new covariance structure model improvement statistics. Sage Focus Editions, 154, 235255.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  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) crossloadings
newpar = '
visual =~ x9
textual =~ x3
'
lavTestScore(fit, add = newpar)
# equivalently, "add" can be a parameter table specifying parameters to free,
# but must include some additional information:
PT.add < data.frame(lhs = c("visual","textual"),
op = c("=~","=~"),
rhs = c("x9","x3"),
user = 10L, # needed to identify new parameters
free = 1, # arbitrary numbers > 0
start = 0) # nullhypothesized value
PT.add
lavTestScore(fit, add = PT.add) # same result as above

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