GOF | R Documentation |
Compute Goodness of Fit distances between models when removing the i_{th}
case.
If mirt is used, then the values will be associated with the unique response patterns instead.
GOF(data, model, M2 = TRUE, progress = TRUE, ...)
## S3 method for class 'GOF'
print(x, ncases = 10, digits = 5, ...)
## S3 method for class 'GOF'
plot(
x,
y = NULL,
main = "Goodness of Fit Distance",
type = c("p", "h"),
ylab = "GOF",
absolute = FALSE,
...
)
data |
matrix or data.frame |
model |
if a single numeric number declares number of factors to extract in
exploratory factor analysis (requires complete dataset, i.e., no missing).
If |
M2 |
logical; use the M2 statistic for when using mirt objects instead of G2? |
progress |
logical; display the progress of the computations in the console? |
... |
additional parameters to be passed |
x |
an object of class |
ncases |
number of extreme cases to display |
digits |
number of digits to round in the printed result |
y |
a |
main |
the main title of the plot |
type |
type of plot to use, default displays points and lines |
ylab |
the y label of the plot |
absolute |
logical; use absolute values instead of deviations? |
Note that GOF
is not limited to confirmatory factor analysis and
can apply to nearly any model being studied
where detection of influential observations is important.
Phil Chalmers rphilip.chalmers@gmail.com
Chalmers, R. P. & Flora, D. B. (2015). faoutlier: An R Package for Detecting Influential Cases in Exploratory and Confirmatory Factor Analysis. Applied Psychological Measurement, 39, 573-574. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0146621615597894")}
Flora, D. B., LaBrish, C. & Chalmers, R. P. (2012). Old and new ideas for data screening and assumption testing for exploratory and confirmatory factor analysis. Frontiers in Psychology, 3, 1-21. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3389/fpsyg.2012.00055")}
gCD
, LD
, obs.resid
,
robustMD
, setCluster
## Not run:
#run all GOF functions using multiple cores
setCluster()
#Exploratory
nfact <- 3
(GOFresult <- GOF(holzinger, nfact))
(GOFresult.outlier <- GOF(holzinger.outlier, nfact))
plot(GOFresult)
plot(GOFresult.outlier)
## include a progress bar
GOFresult <- GOF(holzinger, nfact, progress = TRUE)
#-------------------------------------------------------------------
#Confirmatory with sem
model <- sem::specifyModel()
F1 -> Remndrs, lam11
F1 -> SntComp, lam21
F1 -> WrdMean, lam31
F2 -> MissNum, lam42
F2 -> MxdArit, lam52
F2 -> OddWrds, lam62
F3 -> Boots, lam73
F3 -> Gloves, lam83
F3 -> Hatchts, lam93
F1 <-> F1, NA, 1
F2 <-> F2, NA, 1
F3 <-> F3, NA, 1
(GOFresult <- GOF(holzinger, model))
(GOFresult.outlier <- GOF(holzinger.outlier, model))
plot(GOFresult)
plot(GOFresult.outlier)
#-------------------------------------------------------------------
#Confirmatory with lavaan
model <- 'F1 =~ Remndrs + SntComp + WrdMean
F2 =~ MissNum + MxdArit + OddWrds
F3 =~ Boots + Gloves + Hatchts'
(GOFresult <- GOF(holzinger, model, orthogonal=TRUE))
(GOFresult.outlier <- GOF(holzinger.outlier, model, orthogonal=TRUE))
plot(GOFresult)
plot(GOFresult.outlier)
# categorical data with mirt
library(mirt)
data(LSAT7)
dat <- expand.table(LSAT7)
model <- mirt.model('F = 1-5')
result <- GOF(dat, model)
plot(result)
## End(Not run)
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