View source: R/explore.influence.R
explore.influence | R Documentation |
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.
explore.influence(x, cut.offs = 'default', plot = 'TRUE', cook = 'FALSE', ...)
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 |
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. |
Gianmarco Altoe'
Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley.
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)
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