lavTestScore.mi-deprecated: Score Test for Multiple Imputations
In semTools: Useful Tools for Structural Equation Modeling

lavTestScore.mi-deprecated

R Documentation

Score Test for Multiple Imputations

Description

Score test (or "Lagrange multiplier" test) for lavaan models fitted to multiple imputed data sets. Statistics for releasing one or more fixed or constrained parameters in model can be calculated by pooling the gradient and information matrices pooled across imputed data sets in a method proposed by Mansolf, Jorgensen, & Enders (2020)—analogous to the "D1" Wald test proposed by Li, Meng, Raghunathan, & Rubin's (1991)—or by pooling the complete-data score-test statistics across imputed data sets (i.e., "D2"; Li et al., 1991).

Usage

lavTestScore.mi(object, add = NULL, release = NULL,
                test = c("D2","D1"), scale.W = !asymptotic,
                omit.imps = c("no.conv","no.se"),
                asymptotic = is.null(add),
                univariate = TRUE, cumulative = FALSE,
                epc = FALSE, standardized = epc, cov.std = epc,
                verbose = FALSE, warn = TRUE, information = "expected")

Arguments

`object`	An object of class OLDlavaan.mi.
`add`	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 `integer`s. The indices of the equality constraints that should be released. The indices correspond to the order of the equality constraints as they appear in the parameter table.
`test`	`character` indicating which pooling method to use. `"D1"` requests Mansolf, Jorgensen, & Enders' (2020) proposed Wald-like test for pooling the gradient and information, which are then used to calculate score-test statistics in the usual manner. `"D2"` (default because it is less computationall intensive) requests to pool the complete-data score-test statistics from each imputed data set, then pool them across imputations, described by Li et al. (1991) and Enders (2010).
`scale.W`	`logical`. If `FALSE`, the pooled information matrix is calculated as the weighted sum of the within-imputation and between-imputation components. Otherwise, the pooled information is calculated by scaling the within-imputation component by the average relative increase in variance (ARIV; Enders, 2010, p. 235), which is only consistent when requesting the F test (i.e., `asymptotic = FALSE`. Ignored (irrelevant) if `test = "D2"`.
`omit.imps`	`character` vector specifying criteria for omitting imputations from pooled results. Can include any of `c("no.conv", "no.se", "no.npd")`, the first 2 of which are the default setting, which excludes any imputations that did not converge or for which standard errors could not be computed. The last option (`"no.npd"`) would exclude any imputations which yielded a nonpositive definite covariance matrix for observed or latent variables, which would include any "improper solutions" such as Heywood cases. Specific imputation numbers can also be included in this argument, in case users want to apply their own custom omission criteria (or simulations can use different numbers of imputations without redundantly refitting the model).
`asymptotic`	`logical`. If `FALSE` (default when using `add` to test adding fixed parameters to the model), the pooled test will be returned as an F-distributed variable with numerator (`df1`) and denominator (`df2`) degrees of freedom. If `TRUE`, the pooled F statistic will be multiplied by its `df1` on the assumption that its `df2` is sufficiently large enough that the statistic will be asymptotically `\chi^2` distributed with `df1`. When using the `release` argument, `asymptotic` will be set to `TRUE` because (A)RIV can only be calculated for `add`ed parameters.
`univariate`	`logical`. If `TRUE`, compute the univariate score statistics, one for each constraint.
`cumulative`	`logical`. If `TRUE`, order the univariate score statistics from large to small, and compute a series of multivariate score statistics, each time including an additional constraint in the test.
`epc`	`logical`. If `TRUE`, and we are releasing existing constraints, compute the expected parameter changes for the existing (free) parameters (and any specified with `add`), if all constraints were released. For EPCs associated with a particular (1-df) constraint, only specify one parameter in `add` or one constraint in `release`.
`standardized`	If `TRUE`, two extra columns (`sepc.lv` and `sepc.all`) in the `⁠$epc⁠` table will contain standardized values for the EPCs. See `lavaan::lavTestScore()`.
`cov.std`	`logical`. See `lavaan::standardizedSolution()`.
`verbose`	`logical`. Not used for now.
`warn`	`logical`. If `TRUE`, print warnings if they occur.
`information`	`character` indicating the type of information matrix to use (check `lavaan::lavInspect()` for available options). `"expected"` information is the default, which provides better control of Type I errors.

Value

A list containing at least one data.frame:

⁠$test⁠: The total score test, with columns for the score test statistic (X2), its degrees of freedom (df), its p value under the \chi^2 distribution (p.value), and if asymptotic=FALSE, the average relative invrease in variance (ARIV) used to calculate the denominator df is also returned as a missing-data diagnostic, along with the fraction missing information (FMI = ARIV / (1 + ARIV)).
⁠$uni⁠: Optional (if univariate=TRUE). Each 1-df score test, equivalent to modification indices. Also includes EPCs if epc=TRUE, and RIV and FMI if asymptotic=FALSE.
⁠$cumulative⁠: Optional (if cumulative=TRUE). Cumulative score tests, with ARIV and FMI if asymptotic=FALSE.
⁠$epc⁠: Optional (if epc=TRUE). Parameter estimates, expected parameter changes, and expected parameter values if ALL the tested constraints were freed.

See lavaan::lavTestScore() for details.

Author(s)

Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)

Adapted from lavaan source code, written by Yves Rosseel (Ghent University; Yves.Rosseel@UGent.be)

test = "D1" method proposed by Maxwell Mansolf (University of California, Los Angeles; mamansolf@gmail.com)

References

Bentler, P. M., & Chou, C.-P. (1992). Some new covariance structure model improvement statistics. Sociological Methods & Research, 21(2), 259–282. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0049124192021002006")}

Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford.

Li, K.-H., Meng, X.-L., Raghunathan, T. E., & Rubin, D. B. (1991). Significance levels from repeated p-values with multiply-imputed data. Statistica Sinica, 1(1), 65–92. Retrieved from https://www.jstor.org/stable/24303994

Mansolf, M., Jorgensen, T. D., & Enders, C. K. (2020). A multiple imputation score test for model modification in structural equation models. Psychological Methods, 25(4), 393–411. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/met0000243")}