semTools (version 0.4-13)

fmi: Fraction of Missing Information.

Description

This function takes a list of imputed data sets and estimates the Fraction of Missing Information of the Variances and Means for each variable.

Usage

fmi(dat.imp, method="saturated", varnames=NULL, group=NULL, exclude=NULL, 
digits=3)

Arguments

dat.imp

List of imputed data sets, the function only accept a list of data frames.

method

Specified the model used to estimated the variances and means. Can be one of the following: "saturated" ("sat") or "null", the default is "saturated". See Details for more information.

varnames

A vector of variables names. This argument allow the user to get the fmi of a subset of variables. The function by default will estimate the fmi for all the variables.

group

A variable name defining the groups. This will give the fmi for each group.

exclude

A vector of variables names. These variables will be excluded from the analysis.

digits

Number of decimals to print in the results.

Value

fmi returns a list with the Fraction of Missing Information of the Variances and Means for each variable in the data set. fmi returns a list with the Fraction of Missing Information of the Variances and Means for each variable in the data set.

Details

The function estimates a variance/covariance model for each data set using lavaan. If method = "saturated" the function will estimate all the variances and covariances, if method = "null" the function will only estimate the variances. The saturated model gives more reliable estimates. With big data sets using the saturated model could take a lot of time. In the case of having problems with big data sets it is helpful to select a subset of variables with varnames and/or use the "null" model. The function does not accept character variables.

References

Rubin, D.B. (1987) Multiple Imputation for Nonresponse in Surveys. J. Wiley & Sons, New York.

Savalei, V. & Rhemtulla, M. (2012) On Obtaining Estimates of the Fraction of Missing Information From Full Information Maximum Likelihood, Structural Equation Modeling: A Multidisciplinary Journal, 19:3, 477-494.

Wagner, J. (2010) The Fraction of Missing Information as a Tool for Monitoring the Quality of Survey Data, Public Opinion Quarterly, 74:2, 223-243.

Examples

library(Amelia)
library(lavaan)

modsim <- '
f1 =~ 0.7*y1+0.7*y2+0.7*y3
f2 =~ 0.7*y4+0.7*y5+0.7*y6
f3 =~ 0.7*y7+0.7*y8+0.7*y9'

datsim <- simulateData(modsim,model.type="cfa", meanstructure=TRUE, 
                       std.lv=TRUE, sample.nobs=c(200,200))
randomMiss2 <- rbinom(prod(dim(datsim)), 1, 0.1)
randomMiss2 <- matrix(as.logical(randomMiss2), nrow=nrow(datsim))
randomMiss2[,10] <- FALSE
datsim[randomMiss2] <- NA
datsimMI <- amelia(datsim,m=3,idvars="group")

out1 <- fmi(datsimMI$imputations, exclude="group")
out1
                       
out2 <- fmi(datsimMI$imputations, exclude="group", method="null")
out2
                       
out3 <- fmi(datsimMI$imputations, varnames=c("y1","y2","y3","y4"))
out3

out4 <- fmi(datsimMI$imputations, group="group")
out4