mahalanobis_rerun {semfindr}

Mahalanobis Distance on All Observed Variables

Description

Computes the Mahalanobis distance for each case on all observed variables in a model.

Usage

mahalanobis_rerun(
  fit,
  emNorm_arg = list(estimate.worst = FALSE, criterion = 1e-06)
)

Arguments

fit

It can be the output from lavaan, such as lavaan::cfa() and lavaan::sem(), or the output from lavaan_rerun().

emNorm_arg

No longer used. Kept for backward compatibility.

Details

mahalanobis_rerun() gets a lavaan_rerun() or lavaan::lavaan() output and computes the Mahalanobis distance for each case on all observed variables.

If there are no missing values, stats::mahalanobis() will be used to compute the Mahalanobis distance.

If there are missing values on the observed predictors, the means and variance-covariance matrices will be estimated by maximum likelihood using lavaan::lavCor(). The estimates will be passed to modi::MDmiss() to compute the Mahalanobis distance.

Supports both single-group and multiple-group models. For multiple-group models, the Mahalanobis distance for each case is computed using the means and covariance matrix of the group this case belongs to. (Support for multiple-group models available in 0.1.4.8 and later version).

Value

A md_semfindr-class object, which is a one-column matrix (a column vector) of the Mahalanobis distance for each case. The row names are the case identification values used in lavaan_rerun(). A print method is available for user-friendly output.

Author(s)

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448.

References

Mahalanobis, P. C. (1936). On the generalized distance in statistics. Proceedings of the National Institute of Science of India, 2, 49-55.

Examples

library(lavaan)
dat <- pa_dat
# The model
mod <-
"
m1 ~ a1 * iv1 + a2 * iv2
dv ~ b * m1
a1b := a1 * b
a2b := a2 * b
"
# Fit the model
fit <- lavaan::sem(mod, dat)
summary(fit)
# Fit the model n times. Each time with one case removed.
# For illustration, do this only for selected cases.
fit_rerun <- lavaan_rerun(fit, parallel = FALSE,
                          to_rerun = 1:10)
# Compute the Mahalanobis distance for each case
out <- mahalanobis_rerun(fit_rerun)
# Results excluding a case, for the first few cases
head(out)
# Compute the Mahalanobis distance using stats::mahalanobis()
md1 <- stats::mahalanobis(dat, colMeans(dat), stats::cov(dat))
# Compare the results
head(md1)

# A CFA model

dat <- cfa_dat
mod <-
"
f1 =~  x1 + x2 + x3
f2 =~  x4 + x5 + x6
f1 ~~ f2
"
# Fit the model
fit <- lavaan::cfa(mod, dat)

fit_rerun <- lavaan_rerun(fit, parallel = FALSE,
                          to_rerun = 1:10)
mahalanobis_rerun(fit_rerun)

# A latent variable model

dat <- sem_dat
mod <-
"
f1 =~  x1 + x2 + x3
f2 =~  x4 + x5 + x6
f3 =~  x7 + x8 + x9
f2 ~   a * f1
f3 ~   b * f2
ab := a * b
"
# Fit the model
fit <- lavaan::cfa(mod, dat)

fit_rerun <- lavaan_rerun(fit, parallel = FALSE,
                          to_rerun = 1:10)
mahalanobis_rerun(fit_rerun)