lavaan.miclass  R Documentation 
Class for a lavaan Model Fitted to Multiple Imputations
Description
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.
Usage
## S4 method for signature 'lavaan.mi'
show(object)
## S4 method for signature '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 signature 'lavaan.mi'
nobs(object, total = TRUE)
## S4 method for signature 'lavaan.mi'
coef(object, type = "free", labels = TRUE, omit.imps = c("no.conv", "no.se"))
## S4 method for signature 'lavaan.mi'
vcov(object, type = c("pooled", "between", "within",
"ariv"), scale.W = TRUE, omit.imps = c("no.conv", "no.se"))
## S4 method for signature 'lavaan.mi'
anova(object, ...)
## S4 method for signature 'lavaan.mi'
fitMeasures(object, fit.measures = "all",
baseline.model = NULL, output = "vector", omit.imps = c("no.conv",
"no.se"), ...)
## S4 method for signature 'lavaan.mi'
fitmeasures(object, fit.measures = "all",
baseline.model = NULL, output = "vector", omit.imps = c("no.conv",
"no.se"), ...)
## S4 method for signature 'lavaan.mi'
fitted(object, omit.imps = c("no.conv", "no.se"))
## S4 method for signature 'lavaan.mi'
fitted.values(object, omit.imps = c("no.conv", "no.se"))
## S4 method for signature 'lavaan.mi'
residuals(object, type = c("raw", "cor"), omit.imps = c("no.conv", "no.se"))
## S4 method for signature 'lavaan.mi'
resid(object, type = c("raw", "cor"), omit.imps = c("no.conv", "no.se"))
Arguments
object 
An object of class lavaan.mi

se, ci, level, standardized, rsquare, header, output 
See
parameterEstimates . output
can also be passed to fitMeasures .

fmi 
logical indicating whether to include the Fraction Missing
Information (FMI) for parameter estimates in the summary
output (see Value section).

scale.W 
logical . If TRUE (default), the vcov
method will calculate the pooled covariance matrix by scaling the
withinimputation 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 withinimputation and
betweenimputation 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 ).

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. 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).

asymptotic 
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 χ^2 distribution.

fit.measures, baseline.model 
See fitMeasures .
summary(object, fit.measures = TRUE) will print (but not
return) a table of fit measures to the console.

... 
Additional arguments passed to lavTestLRT.mi , or
subsequently to lavTestLRT .

total 
logical (default: TRUE ) indicating whether the
nobs method should return the total sample size or (if
FALSE ) a vector of group sample sizes.

type 
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 .

labels 
logical indicating whether the coef output
should include parameter labels. Default is TRUE .

Value
coef 
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).

vcov 
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 withinimputations covariance matrix (type = "within" ), the
betweenimputations covariance matrix (type = "between" ), or the
average relative increase in variance (type = "ariv" ) due to
missing data.

fitted.values 
signature(object = "lavaan.mi",
omit.imps = c("no.conv","no.se")) : See lavaan .
Returns modelimplied moments, evaluated at the pooled point estimates.

fitted 
alias for fitted.values

residuals 
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 modelimplied 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.

resid 
alias for residuals

nobs 
signature(object = "lavaan.mi", total = TRUE) : either
the total (default) sample size or a vector of group sample sizes
(total = FALSE ).

anova 
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.

fitMeasures 
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 ... .

fitmeasures 
alias for fitMeasures .

show 
signature(object = "lavaan.mi") : returns a message about
convergence rates and estimation problems (if applicable) across imputed
data sets.

summary 
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 .
Rsquared 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; additional arguments may be
passed via ... to fitMeasures and
subsequently to lavTestLRT.mi .

Slots
coefList
list
of estimated coefficients in matrix format (one
per imputation) as output by lavInspect(fit, "est")
phiList
list
of modelimplied latentvariable covariance
matrices (one per imputation) as output by
lavInspect(fit, "cov.lv")
miList
list
of modification indices output by
modindices
seed
integer
seed set before running imputations
lavListCall
call to lavaanList
used to fit the
model to the list of imputed data sets in @DataList
, stored as a
list
of arguments
imputeCall
call to imputation function (if used), stored as a
list
of arguments
convergence
list
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 (Ψ) is nonpositivedefinite, and (4) the
residual covariance matrix of observed variables (Θ) is
nonpositivedefinite.
lavaanList_slots
All remaining slots are from
lavaanList
, but runMI
only populates a
subset of the list
slots, two of them with custom information:
DataList
The list
of imputed data sets
SampleStatsList
List of output from
lavInspect(fit, "sampstat")
applied to each fitted
model
ParTableList
See lavaanList
vcovList
See lavaanList
testList
See lavaanList
h1List
See lavaanList
. An additional element is
added to the list
: $PT
is the "saturated" model's parameter
table, returned by lav_partable_unrestricted
.
baselineList
See lavaanList
Objects from the Class
See the runMI
function for
details. Wrapper functions include lavaan.mi
,
cfa.mi
, sem.mi
, and growth.mi
.
Author(s)
Terrence D. Jorgensen (University of Amsterdam;
TJorgensen314@gmail.com)
References
Asparouhov, T., & Muthen, B. (2010). Chisquare statistics
with multiple imputation. Technical Report. Retrieved from
http://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 pvalues with multiplyimputed
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
multiplyimputed 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.
Examples
## See ?runMI help page