influence.SEM (version 2.3)

explore.influence: 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 Deltachi, fitinfluence, genCookDist, Likedist or parinfluence functions.

cut.offs

A vector of two numeric elements containing the lower and the upper cut-offs to be considered. If default, the cut-offs are calculated according to the boxplot criterion for outliers (see also, cook).

plot

If TRUE (the default) a graphical representation of case influence is given.

cook

If TRUE, x is interpreted as a vector containing Cook's distances, and so the lower cut-off is forced to be greater or equal to zero.

...

Additional parameters for plot function.

Value

A list with the following components:

n

number of cases.

cook

logical, indicating if x is treated as a vector of Cook's distances.

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.

References

Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.

Examples

# NOT RUN {
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)
# }