Description Usage Arguments Details Value Author(s) References Examples
This function estimates the Fraction of Missing Information (FMI) for summary statistics of each variable, using either an incomplete data set or a list of imputed data sets.
1 2 
data 
Either a single 
method 
character. If 
group 
character. The optional name of a grouping variable, to request FMI in each group. 
ords 
character. Optional vector of names of orderedcategorical
variables, which are not already stored as class 
varnames 
character. Optional vector of variable names, to calculate
FMI for a subset of variables in 
exclude 
character. Optional vector of variable names to exclude from the analysis. 
fewImps 
logical. If 
The function estimates a saturated model with lavaan
for a single incomplete data set using FIML, or with lavaan.mi
for a list of imputed data sets. If method = "saturated"
, FMI will be
estiamted for all summary statistics, which could take a lot of time with
big data sets. If method = "null"
, FMI will only be estimated for
univariate statistics (e.g., means, variances, thresholds). The saturated
model gives more reliable estimates, so it could also help to request a
subset of variables from a large data set.
fmi
returns a list with at least 2 of the following:
Covariances 
A list of symmetric matrices: (1) the estimated/pooled
covariance matrix, or a list of groupspecific matrices (if applicable) and
(2) a matrix of FMI, or a list of groupspecific matrices (if applicable).
Only available if 
Variances 
The
estimated/pooled variance for each numeric variable. Only available if

Means 
The estimated/pooled mean for each numeric variable. 
Thresholds 
The estimated/pooled threshold(s) for each orderedcategorical variable. 
message 
A message indicating caution when the null model is used. 
Mauricio Garnier Villarreal (University of Kansas; mauricio.garniervillarreal@marquette.edu) Terrence Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: Wiley.
Savalei, V. & Rhemtulla, M. (2012). On obtaining estimates of the fraction of missing information from full information maximum likelihood. Structural Equation Modeling, 19(3), 477–494. doi: 10.1080/10705511.2012.687669
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. doi: 10.1093/poq/nfq007
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29  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)
## calculate FMI (using FIML, provide partially observed data set)
(out1 < fmi(HSMiss, exclude = "school"))
(out2 < fmi(HSMiss, exclude = "school", method = "null"))
(out3 < fmi(HSMiss, varnames = c("x5","x6","x7","x8","x9")))
(out4 < fmi(HSMiss, group = "school"))
## Not run:
## orderedcategorical 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
## impute data m = 3 times
library(Amelia)
set.seed(456)
impout < amelia(datCat, m = 3, noms = "g", ords = paste0("u", 1:8), p2s = FALSE)
imps < impout$imputations
## calculate FMI, using list of imputed data sets
fmi(imps, group = "g")
## End(Not run)

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