Last chance! 50% off unlimited learning
Sale ends in
This function fits a lavaan model to a list of imputed data sets, and can
also implement multiple imputation for a single data.frame with
missing observations, using either the Amelia package or the mice package.
runMI(model, data, fun = "lavaan", ..., m, miArgs = list(),
miPackage = "Amelia", seed = 12345)lavaan.mi(model, data, ..., m, miArgs = list(), miPackage = "Amelia",
seed = 12345)
cfa.mi(model, data, ..., m, miArgs = list(), miPackage = "Amelia",
seed = 12345)
sem.mi(model, data, ..., m, miArgs = list(), miPackage = "Amelia",
seed = 12345)
growth.mi(model, data, ..., m, miArgs = list(), miPackage = "Amelia",
seed = 12345)
The analysis model can be specified using lavaan
model.syntax or a parameter table (as returned by
parTable).
A data.frame with missing observations, or a list
of imputed data sets (if data are imputed already). If runMI has
already been called, then imputed data sets are stored in the
@DataList slot, so data can also be a lavaan.mi object
from which the same imputed data will be used for additional analyses.
character. Name of a specific lavaan function used to fit
model to data (i.e., "lavaan", "cfa",
"sem", or "growth"). Only required for runMI.
additional arguments to pass to lavaan or
lavaanList. See also lavOptions.
Note that lavaanList provides parallel computing options, as well as
a FUN argument so the user can extract custom output after the model
is fitted to each imputed data set (see Examples). TIP: If a
custom FUN is used and parallel = "snow" is requested,
the user-supplied function should explicitly call library or use
:: for any functions not part of the base distribution.
integer. Request the number of imputations. Ignored if
data is already a list of imputed data sets or a
lavaan.mi object.
Addition arguments for the multiple-imputation function
(miPackage). The arguments should be put in a list (see example
below). Ignored if data is already a list of imputed data
sets or a lavaan.mi object.
Package to be used for imputation. Currently these
functions only support "Amelia" or "mice" for imputation.
Ignored if data is already a list of imputed data sets or a
lavaan.mi object.
integer. Random number seed to be set before imputing the
data. Ignored if data is already a list of imputed data sets
or a lavaan.mi object.
Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: Wiley.
# NOT RUN {
# }
# NOT RUN {
## impose missing data for example
HSMiss <- HolzingerSwineford1939[ , c(paste("x", 1:9, sep = ""),
"ageyr","agemo","school")]
set.seed(12345)
HSMiss$x5 <- ifelse(HSMiss$x5 <= quantile(HSMiss$x5, .3), NA, HSMiss$x5)
age <- HSMiss$ageyr + HSMiss$agemo/12
HSMiss$x9 <- ifelse(age <= quantile(age, .3), NA, HSMiss$x9)
## specify CFA model from lavaan's ?cfa help page
HS.model <- '
visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9
'
## impute data within runMI...
out1 <- cfa.mi(HS.model, data = HSMiss, m = 20, seed = 12345,
miArgs = list(noms = "school"))
## ... or impute missing data first
library(Amelia)
set.seed(12345)
HS.amelia <- amelia(HSMiss, m = 20, noms = "school", p2s = FALSE)
imps <- HS.amelia$imputations
out2 <- cfa.mi(HS.model, data = imps)
## same results (using the same seed results in the same imputations)
cbind(impute.within = coef(out1), impute.first = coef(out2))
summary(out1, fit.measures = TRUE)
summary(out1, ci = FALSE, fmi = TRUE, output = "data.frame")
summary(out1, ci = FALSE, stand = TRUE, rsq = TRUE)
## model fit. D3 includes information criteria
anova(out1)
## equivalently:
lavTestLRT.mi(out1)
## request D2
anova(out1, test = "D2")
## request fit indices
fitMeasures(out1)
## fit multigroup model without invariance constraints
mgfit.config <- cfa.mi(HS.model, data = imps, estimator = "mlm",
group = "school")
## add invariance constraints, and use previous fit as "data"
mgfit.metric <- cfa.mi(HS.model, data = mgfit.config, estimator = "mlm",
group = "school", group.equal = "loadings")
mgfit.scalar <- cfa.mi(HS.model, data = mgfit.config, estimator = "mlm",
group = "school",
group.equal = c("loadings","intercepts"))
## compare fit of 2 models to test metric invariance
## (scaled likelihood ratio test)
lavTestLRT.mi(mgfit.metric, h1 = mgfit.config)
## To compare multiple models, you must use anova()
anova(mgfit.config, mgfit.metric, mgfit.scalar)
## or compareFit(), which also includes fit indices for comparison
## (optional: name the models)
compareFit(config = mgfit.config, metric = mgfit.metric,
scalar = mgfit.scalar,
argsLRT = list(test = "D2", method = "satorra.bentler.2010"))
## correlation residuals to investigate local misfit
resid(mgfit.scalar, type = "cor.bentler")
## modification indices for fixed parameters, to investigate local misfit
modindices.mi(mgfit.scalar)
## or lavTestScore.mi for modification indices about equality constraints
lavTestScore.mi(mgfit.scalar)
## Wald test of whether latent means are == (fix 3 means to zero in group 2)
eq.means <- ' .p70. == 0
.p71. == 0
.p72. == 0 '
lavTestWald.mi(mgfit.scalar, constraints = eq.means)
## ordered-categorical data
data(datCat)
lapply(datCat, class)
## impose missing values
set.seed(123)
for (i in 1:8) datCat[sample(1:nrow(datCat), size = .1*nrow(datCat)), i] <- NA
catout <- cfa.mi(' f =~ 1*u1 + 1*u2 + 1*u3 + 1*u4 ', data = datCat,
m = 3, seed = 456,
miArgs = list(ords = paste0("u", 1:8), noms = "g"),
FUN = function(fit) {
list(wrmr = lavaan::fitMeasures(fit, "wrmr"),
zeroCells = lavaan::lavInspect(fit, "zero.cell.tables"))
})
summary(catout)
lavTestLRT.mi(catout, test = "D2", pool.robust = TRUE)
fitMeasures(catout, fit.measures = c("rmsea","srmr","cfi"),
test = "D2", pool.robust = TRUE)
## extract custom output
sapply(catout@funList, function(x) x$wrmr) # WRMR for each imputation
catout@funList[[1]]$zeroCells # zero-cell tables for first imputation
catout@funList[[2]]$zeroCells # zero-cell tables for second imputation ...
# }
# NOT RUN {
# }
Run the code above in your browser using DataLab