explore.influence {influence.SEM} | R Documentation |
Explores case influence.
Description
It explores case influence. Cases with extreme values of the considered measure of influence are reported. Extreme values are determined using the boxplot criterion (Tukey, 1977) or user-defined cut-offs. Cases for which deletion leads to a model that does not converge or yelds a solution with negative estimated variances are also reported. In addition, explore.influence provides a graphical representation of case influence.
Usage
explore.influence(x, cut.offs = 'default',
plot = 'TRUE', cook = 'FALSE', ...)
Arguments
x |
A vector containing the influence of each case as returned by
|
cut.offs |
A vector of two numeric elements containing the lower and the upper cut-offs to be considered. If |
plot |
If |
cook |
If |
... |
Additional parameters for |
Value
A list with the following components:
n |
number of cases. |
cook |
logical, indicating if |
cut.low |
the lower cut-off. |
cut.upp |
the upper cut-off. |
not.allowed |
a vector containing cases with negative variance or not converging models. |
less.cut.low |
a vector containing cases with influence value less than the lower cut-off. |
greater.cut.low |
a vector containing cases with influence value greater than the upper cut-off. |
Author(s)
Gianmarco Altoe'
References
Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.
Examples
data("PDII")
model <- "
F1 =~ y1+y2+y3+y4
"
fit0 <- sem(model, data=PDII,std.lv=TRUE)
## not run
# gCD <- genCookDist(model,data=PDII,std.lv=TRUE)
# explore.influence(gCD,cook=TRUE)
##
## not run: this example take several minutes
model <- "
F1 =~ x1+x2+x3
F2 =~ y1+y2+y3+y4
F3 =~ y5+y6+y7+y8
"
# fit0 <- sem(model, data=PDII)
# FI <- fitinfluence('rmsea',model,PDII)
# explore.influence(FI)