cfaHB | R Documentation |
This function generates DFI cutoffs adapted from Hu & Bentler (1999) for multi-factor CFA models using ML estimation.
The default argument is a singular argument: a lavaan
object from the cfa
function.
The function can also accommodate manual entry of the model statement and sample size.
The app-based version of this function can be found at dynamicfit.app.
cfaHB(
model,
n = NULL,
plot = FALSE,
manual = FALSE,
estimator = "ML",
reps = 500
)
## S3 method for class 'cfaHB'
print(x, ...)
model |
This can either be a |
n |
If you entered a |
plot |
Displays distributions of fit indices for each level of misspecification. |
manual |
If you entered a |
estimator |
Which estimator to use within the simulations (enter in quotes). The default is ML. |
reps |
The number of replications used in your simulation. This is set to 500 by default in both the R package and the corresponding Shiny App. |
x |
cfaHB object |
... |
other print parameters |
Dynamic fit index (DFI) cutoffs for SRMR, RMSEA, and CFI.
Melissa G Wolf & Daniel McNeish
Maintainer: Melissa G Wolf <missgord@gmail.com>
#Lavaan object example (manual=FALSE)
dat <- lavaan::HolzingerSwineford1939
lavmod <- "F1 =~ x1 + x2 + x3
F2 =~ x4 + x5 + x6
F3 =~ x7 + x8 + x9"
fit <- lavaan::cfa(lavmod,dat)
cfaHB(fit)
#Manual entry example for a sample size of 400 (manual=TRUE)
manmod <- "F1 =~ .602*Y1 + .805*Y2 + .516*Y3 + .415*Y4
F2 =~ .413*Y5 + -.631*Y6
F1 ~~ .443*F2
Y4 ~~ .301*Y5"
cfaHB(model=manmod,n=400,manual=TRUE)
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