Gets a lavaan_rerun() output and computes the changes
in selected fit measures if a case is included.
Usage
fit_measures_change(
rerun_out,
fit_measures = c("chisq", "cfi", "rmsea", "tli"),
baseline_model = NULL
)Arguments
- rerun_out
The output from
lavaan_rerun().- fit_measures
The argument
fit.measuresused in lavaan::fitMeasures. Default isc("chisq", "cfi", "rmsea", "tli").- baseline_model
The argument
baseline.modelused in lavaan::fitMeasures. Default isNULL.
Value
An fit_measures_change-class object, which is
matrix with the number of columns equals to the number of
requested fit measures, and the number of rows equals to the number
of cases. The row names are the case identification values used in
lavaan_rerun().
A print method is available for user-friendly output.
Details
For each case, fit_measures_change() computes the
differences in selected fit measures with and without this case:
(Fit measure with all case) - (Fit measure without this case).
If the value of a case is positive, including the case increases an estimate.
If the value of a case is negative, including the case decreases an estimate.
Note that an increase is an improvement in fit for goodness of fit measures such as CFI and TLI, but a decrease is an improvement in fit for badness of fit measures such as RMSEA and model chi-square. This is a measure of the influence of a case on a fit measure if it is included.
If the analysis is not admissible or does not converge when a case
is deleted, NAs will be turned for the differences of this
case.
Supports both single-group and multiple-group models. (Support for multiple-group models available in 0.1.4.8 and later version).
References
Pek, J., & MacCallum, R. (2011). Sensitivity analysis in structural equation models: Cases and their influence. Multivariate Behavioral Research, 46(2), 202-228. doi:10.1080/00273171.2011.561068
Author
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448.
Examples
library(lavaan)
# A path model
dat <- pa_dat
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)
#> lavaan 0.6.17 ended normally after 1 iteration
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 5
#>
#> Number of observations 100
#>
#> Model Test User Model:
#>
#> Test statistic 6.711
#> Degrees of freedom 2
#> P-value (Chi-square) 0.035
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Expected
#> Information saturated (h1) model Structured
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|)
#> m1 ~
#> iv1 (a1) 0.215 0.106 2.036 0.042
#> iv2 (a2) 0.522 0.099 5.253 0.000
#> dv ~
#> m1 (b) 0.517 0.106 4.895 0.000
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .m1 0.903 0.128 7.071 0.000
#> .dv 1.321 0.187 7.071 0.000
#>
#> Defined Parameters:
#> Estimate Std.Err z-value P(>|z|)
#> a1b 0.111 0.059 1.880 0.060
#> a2b 0.270 0.075 3.581 0.000
#>
# Fit the model n times. Each time with one case removed.
# For illustration, do this only for four selected cases
fit_rerun <- lavaan_rerun(fit, parallel = FALSE,
to_rerun = 1:10)
#> The expected CPU time is 0.43 second(s).
#> Could be faster if run in parallel.
# Compute the changes in chisq if a case is included
# vs. if this case is removed.
# That is, case influence on model chi-squared.
out <- fit_measures_change(fit_rerun, fit_measures = "chisq")
# Results excluding a case, for the first few cases
head(out)
#> chisq
#> 1 0.15407944
#> 2 -0.01944571
#> 3 -0.41673808
#> 4 -0.15430823
#> 5 0.09730667
#> 6 0.11601736
# Chi-square will all cases included.
(chisq_all <- fitMeasures(fit, c("chisq")))
#> chisq
#> 6.711
# Chi-square with the first case removed
fit_01 <- lavaan::sem(mod, dat[-1, ])
(chisq_no_1 <- fitMeasures(fit_01, c("chisq")))
#> chisq
#> 6.557
# Difference
chisq_all - chisq_no_1
#> chisq
#> 0.154
# Compare to the result from the fit_measures_change
out[1, ]
#> [1] 0.1540794
# 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)
#> The expected CPU time is 0.56 second(s).
#> Could be faster if run in parallel.
out <- fit_measures_change(fit_rerun, fit_measures = "chisq")
head(out)
#> chisq
#> 1 -0.99210274
#> 2 0.90763725
#> 3 -0.25394609
#> 4 -0.03468346
#> 5 -0.63365168
#> 6 0.59956838
(chisq_all <- fitMeasures(fit, c("chisq")))
#> chisq
#> 12.027
fit_01 <- lavaan::sem(mod, dat[-1, ])
(chisq_no_1 <- fitMeasures(fit_01, c("chisq")))
#> chisq
#> 13.019
chisq_all - chisq_no_1
#> chisq
#> -0.992
out[1, ]
#> [1] -0.9921027
# 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::sem(mod, dat)
fit_rerun <- lavaan_rerun(fit, parallel = FALSE,
to_rerun = 1:10)
#> The expected CPU time is 0.64 second(s).
#> Could be faster if run in parallel.
out <- fit_measures_change(fit_rerun, fit_measures = "chisq")
head(out)
#> chisq
#> 1 -0.3385932
#> 2 -0.6367473
#> 3 0.5199304
#> 4 1.2366084
#> 5 -0.5428558
#> 6 -0.2635458
(chisq_all <- fitMeasures(fit, c("chisq")))
#> chisq
#> 41.768
fit_01 <- lavaan::sem(mod, dat[-1, ])
(chisq_no_1 <- fitMeasures(fit_01, c("chisq")))
#> chisq
#> 42.107
chisq_all - chisq_no_1
#> chisq
#> -0.339
out[1, ]
#> [1] -0.3385932