sem (version 3.1-4)

miSem: Estimate a Structural Equation Model By Multiple Imputation

Description

miSem uses the mi function in the mi package to generate multiple imputations of missing data, fitting the specified model to each completed data set.

Usage

miSem(model, ...)

## S3 method for class 'semmod':
miSem(model, ..., data, formula = ~., raw = FALSE, 
    fixed.x=NULL, objective=objectiveML, n.imp=5, n.iter=30, 
    seed=sample(1e+06, 1), mi.args=list())
    
## S3 method for class 'semmodList':
miSem(model, ..., data, formula = ~., group, raw=FALSE, 
        fixed.x=NULL, objective=msemObjectiveML,
        n.imp=5, n.iter=30, seed=sample(1e6, 1), mi.args=list())

## S3 method for class 'miSem':
print(x, ...)

## S3 method for class 'miSem':
summary(object, digits=max(3, getOption("digits") - 2), ...)

Arguments

model

an SEM model-description object of class semmod or semmodList, created by specifyEquations cfa, or

..., formula, raw, fixed.x, objective, group

arguments to be passed to sem.

data

an R data frame, presumably with some missing data (encoded as NA), containing the data for fitting the SEM, possibly along with other variables to use to obtain multiple imputations of missing values. In the case of a multi-group m

n.imp

number of imputations (default 5).

n.iter

number of iterations for the multiple-imputation process (default 30).

seed

seed for the random-number generator (default is an integer sampled from 1 to 1E6); stored in the resulting object.

mi.args

other arguments to be passed to mi.

x, object

an object of class "miSem".

digits

for printing numbers.

Value

• miSem returns an object of class "miSem" with the following components:
• initial.fit
  an sem model object produced using objectiveFIML if raw=TRUE, or the objective function given by the objective argument otherwise.
• mi.fits
  a list of sem model objects, one for each imputed data set.
• imputation
  the object produced by mi, containing the imputed data sets.
• seed
  the seed used for the random number generator.

References

Yu-Sung Su, Andrew Gelman, Jennifer Hill, Masanao Yajima. (2011). ``Multiple imputation with diagnostics (mi) in R: Opening windows into the black box.'' Journal of Statistical Software 45(2).

See Also

sem, mi

Examples

mod.cfa.tests <- cfa(raw=TRUE)
verbal: x1, x2, x3
math: y1, y2, y3

imps <- miSem(mod.cfa.tests, data=Tests, fixed.x="Intercept", raw=TRUE, seed=12345,
              mi.args=list(add.noise=noise.control(post.run.iter=30)))
summary(imps, digits=3) 

library(MBESS) # for data
data(HS.data)

# introduce some missing data:
HS <- HS.data[, c(2,7:10,11:15,20:25,26:30)]
set.seed(12345)
r <- sample(301, 100, replace=TRUE)
c <- sample(2:21, 100, replace=TRUE)
for (i in 1:100) HS[r[i], c[i]] <- NA

mod.hs <- cfa()
spatial: visual, cubes, paper, flags
verbal: general, paragrap, sentence, wordc, wordm
memory: wordr, numberr, figurer, object, numberf, figurew
math: deduct, numeric, problemr, series, arithmet

mod.mg <- multigroupModel(mod.hs, groups=c("Female", "Male")) 
imps.mg <- miSem(mod.mg, data=HS, group="Gender",
             seed=12345, n.iter=50, 
             mi.args=list(add.noise=noise.control(post.run.iter=30)))
summary(imps.mg, digits=3)