runMI {semTools}R Documentation

Fit a lavaan Model to Multiple Imputed Data Sets

Description

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.

Usage

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)

Arguments

model

The analysis model can be specified using lavaan model.syntax or a parameter table (as returned by parTable).

data

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.

fun

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.

m

integer. Request the number of imputations. Ignored if data is already a list of imputed data sets or a lavaan.mi object.

miArgs

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.

miPackage

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.

seed

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.

Value

A lavaan.mi object

Author(s)

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

References

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.

Examples

 ## 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)
summary(out1, ci = FALSE, fmi = TRUE, add.attributes = FALSE)
summary(out1, ci = FALSE, stand = TRUE, rsq = TRUE)

## model fit. D3 includes information criteria
anova(out1)
anova(out1, test = "D2", indices = TRUE) # request D2 and fit indices



## fit multigroup model without invariance constraints
mgfit1 <- cfa.mi(HS.model, data = imps, estimator = "mlm", group = "school")
## add invariance constraints, and use previous fit as "data"
mgfit0 <- cfa.mi(HS.model, data = mgfit1, estimator = "mlm", group = "school",
                 group.equal = c("loadings","intercepts"))

## compare fit (scaled likelihood ratio test)
anova(mgfit0, h1 = mgfit1)

## correlation residuals
resid(mgfit0, type = "cor.bentler")


## use D1 to test a parametrically nested model (whether latent means are ==)
anova(mgfit0, test = "D1", constraints = '
      .p70. == 0
      .p71. == 0
      .p72. == 0')



## 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 =~ u1 + u2 + u3 + 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)
anova(catout, indices = "all") # note the scaled versions of indices, too

## 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 ...


## End(Not run)
[Package semTools version 0.5-0 Index]