lavTestScore: Score test

View source: R/lav_test_score.R

lavTestScoreR Documentation

Score test


Score test (or Lagrange Multiplier test) for releasing one or more fixed or constrained parameters in model.


lavTestScore(object, add = NULL, release = NULL,
             univariate = TRUE, cumulative = FALSE,
             epc = FALSE, standardized = epc, cov.std = epc,
             verbose = FALSE, warn = TRUE, information = "expected")



An object of class lavaan.


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.


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.


Logical. If TRUE, compute the univariate score statistics, one for each constraints.


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.


Logical. If TRUE, and we are releasing existing constraints, compute the expected parameter changes for the existing (free) parameters, for each released constraint.


If TRUE, two extra columns ( and sepc.all) in the $epc table will contain standardized values for the EPCs. In the first column (, 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.


Logical. See standardizedSolution.


Logical. Not used for now.


Logical. If TRUE, print out warnings if they occur.


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.


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.


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 \chi^2 distribution (p.value).

  • $uni: Optional (if univariate=TRUE). Each 1-df score test, equivalent to modification indices. If epc=TRUE when adding parameters (not when releasing constraints), an unstandardized EPC is provided for each added parameter, as would be returned by modificationIndices.

  • $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, 235-255.


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)

# 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) # null-hypothesized value
lavTestScore(fit, add = PT.add) # same result as above

lavaan documentation built on July 26, 2023, 5:08 p.m.