This class extends the '>lavaanList class, created by
fitting a lavaan model to a list of data sets. In this case, the list of
data sets are multiple imputations of missing data.
# S4 method for lavaan.mi
show(object)# S4 method for lavaan.mi
summary(object, se = TRUE, ci = FALSE,
level = 0.95, standardized = FALSE, rsquare = FALSE, fmi = FALSE,
scale.W = !asymptotic, omit.imps = c("no.conv", "no.se"),
asymptotic = FALSE, header = TRUE, output = "text",
fit.measures = FALSE)
# S4 method for lavaan.mi
nobs(object, total = TRUE)
# S4 method for lavaan.mi
coef(object, type = "free", labels = TRUE, omit.imps = c("no.conv", "no.se"))
# S4 method for lavaan.mi
vcov(object, type = c("pooled", "between", "within",
"ariv"), scale.W = TRUE, omit.imps = c("no.conv", "no.se"))
# S4 method for lavaan.mi
anova(object, ...)
# S4 method for lavaan.mi
fitMeasures(object, fit.measures = "all",
baseline.model = NULL, output = "vector", omit.imps = c("no.conv",
"no.se"), ...)
# S4 method for lavaan.mi
fitmeasures(object, fit.measures = "all",
baseline.model = NULL, output = "vector", omit.imps = c("no.conv",
"no.se"), ...)
# S4 method for lavaan.mi
fitted(object, omit.imps = c("no.conv", "no.se"))
# S4 method for lavaan.mi
fitted.values(object, omit.imps = c("no.conv", "no.se"))
# S4 method for lavaan.mi
residuals(object, type = c("raw", "cor"), omit.imps = c("no.conv", "no.se"))
# S4 method for lavaan.mi
resid(object, type = c("raw", "cor"), omit.imps = c("no.conv", "no.se"))
An object of class lavaan.mi
See
parameterEstimates. output
can also be passed to fitMeasures.
logical indicating whether to include the Fraction Missing
Information (FMI) for parameter estimates in the summary
output (see Value section).
logical. If TRUE (default), the vcov
method will calculate the pooled covariance matrix by scaling the
within-imputation component by the ARIV (see Enders, 2010, p. 235,
for definition and formula). Otherwise, the pooled matrix is
calculated as the weighted sum of the within-imputation and
between-imputation components (see Enders, 2010, ch. 8, for details).
This in turn affects how the summary method calcualtes its
pooled standard errors, as well as the Wald test
(lavTestWald.mi).
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. NPD solutions are not excluded by default because
they are likely to occur due to sampling error, especially in small
samples. However, gross model misspecification could also cause
NPD solutions, users can compare pooled results with and without
this setting as a sensitivity analysis to see whether some
imputations warrant further investigation. 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).
logical. If FALSE (typically a default, but
see Value section for details using various methods), pooled
tests (of fit or pooled estimates) will be F or t
statistics with associated degrees of freedom (df). If
TRUE, the (denominator) df are assumed to be
sufficiently large for a t statistic to follow a normal
distribution, so it is printed as a z statisic; likewise,
F times its numerator df is printed, assumed to follow
a \(\chi^2\) distribution.
See fitMeasures.
summary(object, fit.measures = TRUE) will print (but not
return) a table of fit measures to the console.
logical (default: TRUE) indicating whether the
nobs method should return the total sample size or (if
FALSE) a vector of group sample sizes.
The meaning of this argument varies depending on which method it
it used for. Find detailed descriptions in the Value section
under coef, vcov, and residuals.
logical indicating whether the coef output
should include parameter labels. Default is TRUE.
Additional arguments passed to lavTestLRT.mi, or
subsequently to lavTestLRT.
signature(object = "lavaan.mi", type = "free",
labels = TRUE, omit.imps = c("no.conv","no.se")):
See '>lavaan. Returns the pooled point estimates (i.e.,
averaged across imputed data sets; see Rubin, 1987).
signature(object = "lavaan.mi", scale.W = TRUE,
omit.imps = c("no.conv","no.se"),
type = c("pooled","between","within","ariv")): By default, returns the
pooled covariance matrix of parameter estimates (type = "pooled"),
the within-imputations covariance matrix (type = "within"), the
between-imputations covariance matrix (type = "between"), or the
average relative increase in variance (type = "ariv") due to
missing data.
signature(object = "lavaan.mi",
omit.imps = c("no.conv","no.se")): See '>lavaan.
Returns model-implied moments, evaluated at the pooled point estimates.
alias for fitted.values
signature(object = "lavaan.mi",
type = c("raw","cor"), omit.imps = c("no.conv","no.se")):
See '>lavaan. By default (type = "raw"), returns
the difference between the model-implied moments from fitted.values
and the pooled observed moments (i.e., averaged across imputed data sets).
Standardized residuals are also available, using Bollen's
(type = "cor" or "cor.bollen") or Bentler's
(type = "cor.bentler") formulas.
alias for residuals
signature(object = "lavaan.mi", total = TRUE): either
the total (default) sample size or a vector of group sample sizes
(total = FALSE).
signature(object = "lavaan.mi", ...):
Returns a test of model fit for a single model (object) or test(s)
of the difference(s) in fit between nested models passed via ....
See lavTestLRT.mi and compareFit for details.
signature(object = "lavaan.mi",
fit.measures = "all", baseline.model = NULL, output = "vector",
omit.imps = c("no.conv","no.se"), ...): See lavaan's
fitMeasures for details. Pass additional arguments
to lavTestLRT.mi via ....
alias for fitMeasures.
signature(object = "lavaan.mi"): returns a message about
convergence rates and estimation problems (if applicable) across imputed
data sets.
signature(object = "lavaan.mi", se = TRUE, ci = FALSE,
level = .95, standardized = FALSE, rsquare = FALSE, fmi = FALSE,
scale.W = !asymptotic, omit.imps = c("no.conv","no.se"),
asymptotic = FALSE, header = TRUE, output = "text", fit.measures = FALSE):
see parameterEstimates for details.
By default, summary returns pooled point and SE
estimates, along with t test statistics and their associated
df and p values. If ci = TRUE, confidence intervales
are returned with the specified confidence level (default 95% CI).
If asymptotic = TRUE, z instead of t tests are
returned. standardized solution(s) can also be requested by name
("std.lv" or "std.all") or both are returned with TRUE.
R-squared for endogenous variables can be requested, as well as the
Fraction Missing Information (FMI) for parameter estimates. By default, the
output will appear like lavaan's summary output, but if
output == "data.frame", the returned data.frame will resemble
the parameterEstimates output. The scale.W argument is
passed to vcov (see description above).
Setting fit.measures=TRUE will additionally print fit measures to
the console, but they will not be returned.
coefListlist of estimated coefficients in matrix format (one
per imputation) as output by lavInspect(fit, "est")
phiListlist of model-implied latent-variable covariance
matrices (one per imputation) as output by
lavInspect(fit, "cov.lv")
miListlist of modification indices output by
modindices
seedinteger seed set before running imputations
lavListCallcall to lavaanList used to fit the
model to the list of imputed data sets in @DataList, stored as a
list of arguments
imputeCallcall to imputation function (if used), stored as a
list of arguments
convergencelist of logical vectors indicating whether,
for each imputed data set, (1) the model converged on a solution, (2)
SEs could be calculated, (3) the (residual) covariance matrix of
latent variables (\(\Psi\)) is non-positive-definite, and (4) the
residual covariance matrix of observed variables (\(\Theta\)) is
non-positive-definite.
lavaanList_slotsAll remaining slots are from
'>lavaanList, but runMI only populates a
subset of the list slots, two of them with custom information:
DataListThe list of imputed data sets
SampleStatsListList of output from
lavInspect(fit, "sampstat") applied to each fitted
model
ParTableListvcovListtestListh1ListSee '>lavaanList. An additional element is
added to the list: $PT is the "saturated" model's parameter
table, returned by lav_partable_unrestricted.
baselineListAsparouhov, T., & Muthen, B. (2010). Chi-square statistics with multiple imputation. Technical Report. Retrieved from www.statmodel.com
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
Meng, X.-L., & Rubin, D. B. (1992). Performing likelihood ratio tests with multiply-imputed data sets. Biometrika, 79(1), 103--111. doi:10.2307/2337151
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: Wiley.
# NOT RUN {
## See ?runMI help page
# }
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